Self-aware and correcting heterogenous platform incorporating integrated semiconductor processing modules and method for using same

ABSTRACT

This disclosure relates to a high volume manufacturing system for processing and measuring workpieces in a semiconductor processing sequence without leaving the system&#39;s controlled environment (e.g., sub-atmospheric pressure). The system includes an active interdiction control system to implement corrective processing within the system when a non-conformity is detected. The corrective processing can include a remedial process sequence to correct the non-conformity or compensate for the non-conformity during subsequent process. The non-conformity may be associated with fabrication measurement data, process parameter data, and/or platform performance data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication No. 62/645,685, filed on Mar. 20, 2018, entitled “SubstrateProcessing Tool with Integrated Metrology and Method of Using,” U.S.Provisional Application No. 62/787,607, filed on Jan. 2, 2019, entitled“Self-Aware and Correcting Heterogeneous Platform incorporatingIntegrated Semiconductor Processing Modules and Method for using same,”U.S. Provisional Application No. 62/787,608, filed on Jan. 2, 2019,entitled “Self-Aware and Correcting Heterogeneous Platform incorporatingIntegrated Semiconductor Processing Modules and Method for using same,”and U.S. Provisional Application No. 62/788,195, filed on Jan. 4, 2019,entitled “Substrate Processing Tool with Integrated Metrology and Methodof using,” which are incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to substrate processing, and moreparticularly, to an integrated substrate processing system and modulesconfigured for performing integrated substrate processing and substratemeasurement and metrology in an efficient platform for providingcorrective processing.

BACKGROUND OF THE INVENTION

The semiconductor manufacturing industry is evolving through anotherrevolution in the demand for greater yield and the increased complexityof the device structures formed on substrates. Furthermore, the industryis driven by increased computerization and digitization of variousprocesses for device fabrication.

More specifically, in the processing of substrates for formingintegrated circuits, it has become more critical to increase yield andincrease the efficiency and throughput in the fabrication process. Suchefficiency is realized in the reduced time spent in the fabricationprocess, more accurate and fault-free processes, as well as the reducedcosts resulting from such improvements. It is further desirable todetermine that the processing steps are proceeding properly, and thevarious layers and features created are of the proper dimension,alignment and consistency. That is, the sooner a fault can be detectedand addressed, such as by being corrected or ameliorated in furtherprocessing or the substrate being ejected, the more efficient theprocess becomes.

Not only must yield be maintained and increased, but it must occurwithin the fabrication of smaller and more complex devices. For example,as smaller circuits such as transistors are manufactured, the criticaldimension (CD) or resolution of patterned features is becoming morechallenging to produce. Self-aligned patterning needs to replaceoverlay-driven patterning so that cost-effective scaling can continueeven after the introduction of extreme ultraviolet (EUV) lithography.Patterning options that enable reduced variability, extend scaling andenhanced CD and process control are needed. However, it has becomeextremely difficult to produce scaled devices at reasonably low cost.Selective deposition, together with selective etch, can significantlyreduce the cost associated with advanced patterning. Selectivedeposition of thin films such as gap fill, area selective deposition ofdielectrics and metals on specific substrates, and selective hard masksare key steps in patterning in highly scaled technology.

With such fabrication technologies, it is necessary to monitor thevarious processes to ensure that the etching and deposition steps arewithin specification and to detect variations in the processes.Variations in a manufacturing process can include deviations from theintended or designed target specifications for the manufacturingprocess. Generally, the source of variation can be classified as eithera defect, such as particle contamination, or a parametric variation ornon-conformity in a pattern or device. Examples of such parametricvariations include, a shift in CD, in profile, in depth, in thickness,etc. Such variations can occur as lot-to-lot variations, assubstrate-to-substrate (within lot) variations, within-substratevariations, and within-die variations.

Accordingly, device makers currently use a significant amount offabrication resources qualifying and monitoring the various processes.However, such resources do not contribute to throughput and production,and as a result, are purely costs for the fabricators. Furthermore, whena process goes out of specification, and the features of the substrateare not properly fabricated, it may be necessary to remove the substratefrom production. Currently, for qualifying and monitoring fabricationprocesses, device makers utilize various separate measurement and/ormetrology steps. Implementation of metrology steps between processsteps, or between important process sequences, is used but currentlyinvolves compromising substrate and the process environment control.

Specifically, for current metrology steps, the substrates are removedfrom the processing environment which is under vacuum, are moved atatmosphere to a metrology system or kiosk, and then returned to theprocessing environment. With traditional measurements made betweenprocessing steps and processing chambers, air and contaminants areexposed to the process and the substrates. This may chemically orotherwise modify one or more of the processed layers. This alsointroduces uncertainty in any measurements where the substrate has to bebrought out of a vacuum or other controlled environment and thenintroduced into the metrology kiosk. Accordingly, fabricators may not becertain that they are measuring the parameters that they believe theyare measuring. As such, with smaller feature sizes in three-dimensionaldevices/architectures, current monitoring technologies and measurementand metrology processes are inadequate.

Still further, because the metrology process is intrusive to theproduction cycle and limits the efficiency and throughput of thefabrication process, such metrology steps are minimized so as to notsignificantly affect throughput. As a result, there can often be a lagin time between a particular process going out of specification and therecognition of that fact. This further detrimentally affects yield.

An additional drawback with current fabrication protocols is the needfor constant removal of substrates from platforms, such as systems withdeposition modules, and the transport to other platforms, such assystems with etch modules or some other processing modules. Sincefabrication involves large sequences of various deposition and etch andother processing steps, the need to remove substrates from a system,transport, re-introduce into another system, reapply vacuum or someother controlled environment introduces further time and cost into theprocess. The intermediary measurement or metrology processes onlyexacerbate the time and cost for fabrication. The constant removal fromcontrolled environments as well as the transport further introducesincidences of substrate breakage and contamination as well.

Still further, as may be appreciated, the numerous systems and platformsinvolved for the deposition steps, etch steps and other processingsteps, as well as separate measurement/metrology systems, creates asignificant hardware footprint within clean room environments where realestate or floor space is already expensive and at a premium.

Accordingly, it is desirable to improve substrate processing involvingsmaller circuit devices and features while maintaining the ability toqualify and monitor the process during production. It is desirable toreduce the number of junctures during fabrication wherein substrates aretaken out of vacuum to atmosphere, and then must be subsequently placedback under vacuum in a processing chamber for further processing. It isfurther desirable to reduce the lag time between the process orsubstrate going out of specification, and the recognition of that issueby a fabricator or device maker so that they can respond more quickly.It is further desirable to continue to automate equipment and utilizeprocess data to lessen human intervention in the fabrication process,leading to prescriptive optimization and full decision automation.

Therefore, there is an overall need to address the drawbacks in thecurrent fabrication processes and equipment platforms.

SUMMARY OF THE INVENTION

This disclosure relates to a high volume manufacturing platform whichincorporates metrology instruments integrated to measure workpiecesbefore and/or after being treated in the platform's processing chambers.Transfer chambers connected to the process chambers are integrated withthe metrology sensors to enable measurements being done within theplatform, and not a stand-alone metrology tool. The measured data maydetect non-conforming workpiece attributes caused by the previousprocessing on the platform or previous processing tools. The platformmay also monitor process performance data to detect processnon-conformities based on in-situ or ex-situ processing measurements.The measured and/or monitored data may be used by an active interdictioncontrol system to implement a corrective process sequence to remediateor compensate for the non-conformities.

In one embodiment, the manufacturing platform may include a plurality ofprocessing modules coupled to transfer module configured for movingworkpieces between the processing modules during a process sequence. Theplatform may host one or more measurement modules to measure workpieceattributes during the process sequence. The measured data may detect anout-of-tolerance condition for a workpiece attribute, the attributeincluding, particles, thickness, a critical dimension, a surfaceroughness, a film or surface composition, a feature profile, a patternedge placement, a void, a loss of selectivity, a measure ofnon-uniformity, or a loading effect, or any combination of two or morethereof.

The platform may include an active interdiction control system tocollect, analyze, by characterizing the measured data and performance ofthe process sequence and/or determining an action plan to correct theprocess sequence in the event the non-conformity exists. The activeinterdiction system comprises an interaction component that receives themeasured data, the interaction component including an adaptor componentthat packages the measured data and conveys packaged data and anautonomous learning component that receives the packaged data andgenerates knowledge that characterizes the packaged data and performanceof the process sequence. The active interdiction system may use thegenerated knowledge to create a corrective processing sequence toremediate or compensate for the detected non-conformities.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of embodiments of the invention and many ofthe attendant advantages thereof will become readily apparent withreference to the following detailed description, particularly whenconsidered in conjunction with the accompanying drawings, in which:

FIG. 1 is a schematic illustration of a semiconductor fabricationprocess flow for implementing the invention.

FIG. 2 is a schematic illustration of a semiconductor fabricationprocess flow that implements an embodiment of the invention.

FIG. 3 is a schematic illustration of a semiconductor fabricationplatform in accordance with an embodiment of the invention.

FIG. 4 is a top view of a common platform incorporating process andmeasurement modules in accordance with an embodiment of the invention.

FIG. 5A is a top view of a common platform incorporating process andmeasurement modules in accordance with another embodiment of theinvention.

FIG. 5B is a side view in partial cross-section of a measurement moduleincorporated in a common platform in accordance with an embodiment ofthe invention.

FIG. 5C is side view in partial cross-section of a measurement moduleincorporated in a common platform in accordance with another embodimentof the invention.

FIG. 5D is a side view in partial cross-section of a measurement moduleincorporated in a common platform in accordance with another embodimentof the invention

FIG. 5E is top diagrammatic view of an inspection system in accordancewith an embodiment of the invention.

FIG. 5F is a side view in partial cross-section of a measurement moduleincorporated in a common platform in accordance with another embodimentof the invention.

FIG. 6A is a top view of a common platform incorporating process andmeasurement modules in accordance with another embodiment of theinvention.

FIG. 6B is a side view in partial cross-section of a measurement moduleincorporated in a common platform in accordance with an embodiment ofthe invention.

FIG. 7A is a top view of a common platform incorporating process and ameasurement transfer module in accordance with another embodiment of theinvention.

FIG. 7B is a side view in partial cross-section of a transfermeasurement module incorporated in a common platform in accordance withan embodiment of the invention.

FIG. 7C is a side view in partial cross-section of a transfermeasurement module incorporated in a common platform in accordance withanother embodiment of the invention.

FIG. 7D is a top view of a workpiece transfer mechanism in accordancewith an embodiment of the invention.

FIG. 7E is a side view of the workpiece transfer mechanism of FIG. 7D.

FIGS. 7F and 7G are schematic views of an inspection systems for use inmeasurement modules in accordance with the invention.

FIGS. 7H and 7I are perspective and side cross-sectional views,respectively, of a support platform for workpiece measurement inaccordance with the invention.

FIG. 8 is a schematic illustration of a semiconductor fabricationplatform in accordance with an embodiment of the invention.

FIG. 8A is top view of a common platform incorporating process and ameasurement transfer module in accordance with an embodiment of theinvention.

FIG. 8B is a top view of a common platform incorporating process and ameasurement transfer module in accordance with another embodiment of theinvention.

FIG. 9 is a top view of a common platform incorporating process and ameasurement transfer module in accordance with another embodiment of theinvention.

FIGS. 9A and 9B are side views in partial cross-section of transfermeasurement modules incorporated in a common platform in accordance withanother embodiment of the invention.

FIG. 10A is a schematic illustration of a semiconductor fabricationplatform in accordance with an embodiment of the invention.

FIG. 10B is a schematic illustration of a semiconductor fabricationplatform in accordance with another embodiment of the invention.

FIG. 10C is a schematic illustration of a processing module for use insemiconductor fabrication in accordance with an embodiment of theinvention.

FIG. 10D is a schematic illustration of a processing module for use insemiconductor fabrication in accordance with an embodiment of theinvention.

FIG. 10E is a schematic illustration of a processing module for use insemiconductor fabrication in accordance with an embodiment of theinvention.

FIG. 11 is a schematic block diagram of an active interdiction controlsystem and components in accordance with embodiments of the invention.

FIG. 12 is a schematic block diagram of a computer system forimplementing an interdiction control system in accordance withembodiments of the invention.

FIGS. 13A-13E illustrate schematic cross-sectional views of a workpiecewith area selective film formation in accordance with embodiments of theinvention.

FIG. 14 is a process flow diagram for performing integrated workpieceprocessing, measurement/metrology and active interdiction in accordancewith embodiments of the invention.

FIG. 14A is a process flow diagram for performing integrated workpieceprocessing, measurement/metrology and active interdiction in accordancewith embodiments of the invention.

FIG. 14B is a process flow diagram for performing integrated workpieceprocessing, measurement/metrology and active interdiction in accordancewith embodiments of the invention.

FIG. 15 is a flow diagram for performing measurement and analysis forproviding active interdiction in accordance with the invention.

FIG. 16 is a flow diagram of selective paths of active interdiction.

FIG. 17 illustrates a high level block diagram of an autonomousbiologically based learning tool.

FIG. 18 is a diagram that delineates contextual goal adaptationaccording to aspects described herein.

FIG. 19 illustrates a high level block diagram of an example autonomousbiologically based learning tool.

FIG. 20 is a diagram of an example tool system for semiconductormanufacturing that can exploit an autonomous biologically based learningsystem.

FIG. 21 illustrates a high level block diagram of example architectureof autonomous biologically based learning system.

FIGS. 22A and 22B illustrate, respectively an example autobot componentand an example autobot architecture.

FIG. 23 illustrates an example architecture of a self-awarenesscomponent of an autonomous biologically based learning system.

FIG. 24 is a diagram of example autobots that operate in an awarenessworking memory according to aspects described herein.

FIG. 25 illustrates an example embodiment of a self-conceptualizationcomponent of an autonomous biologically based learning system.

FIG. 26 illustrates and example embodiment of a self-optimizationcomponent in an autonomous biologically based learning system.

FIGS. 27A and 27B illustrate an example dependency graph with a singleprediction comparator and two recipe comparators, respectively,generated according to an aspect of the subject disclosure.

FIG. 28 illustrates a diagram of an example group deployment ofautonomous biologically based learning tool systems in accordance withaspects described herein.

FIG. 29 illustrates a diagram of a conglomerate deployment of autonomoustool systems according to aspects described herein.

FIG. 30 illustrates the modular and recursively-coupled characters ofautonomous tool systems described in the subject specification.

FIG. 31 illustrates an example system that assesses, and reports on, amulti-station process for asset generation in accordance with aspectsdescribed herein.

FIG. 32 is a block diagram of an example autonomous system which candistribute output assets that are autonomously generated by a toolconglomerate system in accordance with aspects set forth herein.

FIG. 33 illustrates an example of autonomously determined distributionsteps, from design to manufacturing and to marketing, for an asset(e.g., a finished product, a partially finished product, . . . ).

FIG. 34 presents a flowchart of an example method for biologically basedautonomous learning according to aspects described herein.

FIG. 35 presents a flowchart of an example method for adjusting asituation score of a concept according to an aspect described in thesubject specification.

FIG. 36 presents a flowchart of an example method for generatingknowledge in accordance with an aspect set forth herein.

FIG. 37 presents a flowchart of an example method for asset distributionaccording to aspects disclosed herein.

DETAILED DESCRIPTION OF EMBODIMENTS

According to embodiments described herein, equipment modules areintegrated on a common manufacturing platform to facilitate critical,end-to-end process flows without disrupting a vacuum or controlledenvironment, otherwise not achievable on conventional platforms. Thecommon platform integrates heterogeneous equipment and processingmodules with metrology or measurement modules that monitor substratefabricator progress between process steps without disrupting the vacuumor controlled environment. The integrated metrology or measurementcomponents, together with in-situ equipment module diagnostics andvirtual metrology, collects data on-wafer, and collects equipment dataupstream and downstream within the process sequence flow. The data iscombined with equipment and process control models to create actionableinformation for predicting and detecting faults, predicting maintenance,stabilizing process variations, and correcting processes to achieveproductivity and yield. To establish the equipment and process controlmodels, all data is integrated, i.e., data from equipment module logs,transfer module logs, platform logs, fab host, etc., and combined withanalytical techniques, including deep learning algorithms, to understandthe relationships between equipment and process control parameters, andthe process result on the substrate or wafer. An active interdictioncontrol system that might be hosted in part in the common platformperforms corrective processing in upstream and downstream processingmodules to address detected non-conformities, defects, or othervariations.

In accordance with the invention, data utilization is provided with ahierarchical knowledge base built on equipment, data, and knowledge,established process technology, sensors and metrology data includingvirtual metrology data to monitor equipment and process status. Dataprocess technology and algorithm know-how, and process and equipmentmodels are used to link equipment and process control parameters toyield and productivity. Holistic equipment and process control modelscan be developed. Process simulation, measurement and metrology data anddiagnostics, and data analysis leads to predictive and preventiveprocessing and action that can improve equipment up-time, optimizeprocess, and control process variations. This improves yield andproductivity. The invention can use the data collected for providingvirtual metrology (VM), run-to-run (R2R) control to monitor and controlprocess variations, statistical process control (SPC) to alert operatorsthat equipment and/or process is operating outside control limits,advanced process control (APC), fault detection and classification(FDC), fault prediction, equipment health monitoring (EHM), predictivemaintenance (PM), predictive scheduling, yield prediction, among otheradvantages.

Embodiments of the invention describe a platform of processing modulesand tools configured for performing integrated substrate processing andsubstrate metrology, and methods of processing a substrate or workpiece.Herein, the workpieces that are the subject of processing may bereferred to as ‘workpiece” “substrate” or “wafer.” The workpieces beingprocessed remain under vacuum. That is, measurement/metrology processesand modules are integrated together with processing modules and systems,processing chambers and tools, and overall manufacturing platforms to beutilized before, during or after processing, in a vacuum environment forcollecting data associated with an attribute on a workpiece, such asattributes of the workpiece surfaces, features, and devices thereon. Thecollected measurement/metrology data is then utilized to affect theprocessing steps, the processing module operation, and overallprocessing system, in real time with respect to the processing steps.The invention will correctively adapt or tune, or otherwise affect, oneor more of the processing steps/processing modules of the system to keepthe substrate in specification or to correct features or layers out ofspecification. The system steps and modules are not only affected goingforward in the processing, but also previous processing steps andmodules may be adapted through feedback in the system to correct aprocessing step or process chamber for future substrates. The inventionmay process the substrate through the most recent processing step, suchas an etch step or film forming or deposition step, and then immediatelycollect measurement/metrology data. As used herein, measurementdata/steps and metrology data/steps are referred to synonymously togenerally mean data measured in accordance with the invention. The datais then processed to detect non-conformities or defects, and a futureprocessing step may be affected to take any necessary corrective actionto address a substrate found to be out of specification or defective insome manner. A future processing step, for example, might includereturning the substrate to the immediately previous processing module,affecting a future processing step in another processing chamber toaddress the measurement/metrology data or introducing one or moreadditional processing steps in the processing sequence to bring thesubstrate back into specification. If the metrology data determines thatthe substrate may not be further processed to bring it intospecification or to correct a non-conformity it might be ejected fromthe manufacturing platform much earlier in the process to avoidunnecessary further processing.

For purposes of explanation, specific numbers, materials, andconfigurations are set forth in order to provide a thoroughunderstanding of the invention. Nevertheless, the invention may bepracticed without specific details. Furthermore, it is understood thatthe various embodiments shown in the figures are illustrativerepresentations and are not necessarily drawn to scale. In referencingthe figures, like numerals refer to like parts throughout.

Reference throughout this specification to “one embodiment” or “anembodiment” or variation thereof means that a particular feature,structure, material, or characteristic described in connection with theembodiment is included in at least one embodiment of the invention butdoes not denote that it is present in every embodiment. Thus, thephrases such as “in one embodiment” or “in an embodiment” that mayappear in various places throughout this specification are notnecessarily referring to the same embodiment of the invention.Furthermore, the particular features, structures, materials, orcharacteristics may be combined in any suitable manner in one or moreembodiments. Various additional layers and/or structures may be includedand/or described features may be omitted in other embodiments.

Additionally, it is to be understood that “a” or “an” may mean “one ormore” unless explicitly stated otherwise.

Various operations will be described as multiple discrete operations inturn, in a manner that is most helpful in understanding the invention.However, the order of description should not be construed as to implythat these operations are necessarily order dependent. In particular,these operations need not be performed in the order of presentation.Operations described may be performed in a different order than thedescribed embodiment. Various additional operations may be performedand/or described operations may be omitted in additional embodiments.

As used herein, the term “substrate” means and includes a base materialor construction upon which materials are formed. It will be appreciatedthat the substrate may include a single material, a plurality of layersof different materials, a layer or layers having regions of differentmaterials or different structures in them, etc. These materials mayinclude semiconductors, insulators, conductors, or combinations thereof.For example, the substrate may be a semiconductor substrate, a basesemiconductor layer on a supporting structure, a metal electrode or asemiconductor substrate having one or more layers, structures or regionsformed thereon. The substrate may be a conventional silicon substrate orother bulk substrate comprising a layer of semi-conductive material. Asused herein, the term “bulk substrate” means and includes not onlysilicon wafers, but also silicon-on-insulator (“SOI”) substrates, suchas silicon-on-sapphire (“SOS”) substrates and silicon-on-glass (“SOG”)substrates, epitaxial layers of silicon on a base semiconductorfoundation, and other semiconductor or optoelectronic materials, such assilicon-germanium, germanium, gallium arsenide, gallium nitride, andindium phosphide. The substrate may be doped or undoped.

As used herein the term “workpiece” may more generally refer to acomposition of materials or layers that are formed on a substrate duringone or more phases of a semiconductor device manufacturing process, theworkpiece ultimately comprising the semiconductor device(s) at a finalstage of the processing. In any regard, the terms ‘workpiece”“substrate” or “wafer” are not limiting to the invention.

The present embodiments include methods that utilize a commonmanufacturing platform in which multiple process steps are performed onthe common platform within a controlled environment, for example,without breaking vacuum between operations. The integrated end-to-endplatform includes both etching modules and film-forming modules and isconfigured to transfer a workpiece from one module to another whilemaintaining the workpiece in a controlled environment, e.g., withoutbreaking vacuum or leaving an inert gas protective environment, and thusavoiding exposure to an ambient environment. Any of a number ofprocesses may be carried out on the common manufacturing platform, andthe integrated end-to-end platform will enable high-volume manufacturingat reduced cost with improvement to yield, defectivity levels and EPE.

As used herein, a “film-forming module” refers to any type of processingtool for depositing or growing a film or layer on a workpiece in aprocess chamber. The film-forming module may be a single wafer tool, abatch processing tool, or a semi-batch processing tool. The types offilm deposition or growth that may be performed in the film-formingmodule include, by way of example and not limitation, chemical vapordeposition, plasma-enhanced or plasma-assisted chemical vapordeposition, atomic layer deposition, physical vapor deposition, thermaloxidation or nitridation, etc., and the process may be isotropic,anisotropic, conformal, selective, blanket, etc.

As used herein, an “etching module” refers to any type of processingtool for removing all or a portion of a film, layer, residue orcontaminant on a workpiece in a process chamber. The etching module maybe a single wafer tool, a batch processing tool, or a semi-batchprocessing tool. The types of etching that may be performed in theetching module include, by way of example and not limitation, chemicaloxide removal (COR), dry (plasma) etching, reactive ion etching, wetetching using immersion or non-immersion techniques, atomic layeretching, chemical-mechanical polishing, cleaning, ashing, lithography,etc., and the process may be isotropic, anisotropic, selective, etc.

As used herein, “module” generally refers to a processing tool with allof its hardware and software collectively, including the processchamber, substrate holder and movement mechanisms, gas supply anddistribution systems, pumping systems, electrical systems andcontrollers, etc. Such details of the modules are known in the art andtherefore not discussed herein.

“Controlled environment” as used herein refers to an environment inwhich the ambient atmosphere is evacuated and either replaced with apurified inert gas or a low-pressure vacuum environment. A vacuumenvironment is well below atmospheric pressure and is generallyunderstood to be 100 Torr or less, for example 5 Torr or less.

FIG. 1 shows an example of a typical semiconductor fabrication process100 for reference that may be improved with the present invention.Before the fabrication process itself, the overall design 102 of thesemiconductor workpiece or substrate and the microelectronic devicesformed therein is produced. A layout is produced from the design, andthe layout includes sets of patterns that will be transferred to thestacked layers of materials that are applied to the semiconductorworkpiece during its fabrication in a processing sequence to form thevarious circuits and devices on the substrate. Since thedesign/processing sequence 102 affects and informs various portions ofthe fabrication process, it is depicted with a general arrow 104pointing to the fabrication process rather than to particular stepsthereof.

The fabrication process 100 illustrates one exemplary process flow orprocessing sequence which is used several times to deposit or form filmson a substrate and pattern them using a variety of lithography and etchtechniques. Such general fabrication steps and processes are known to aperson of ordinary skill in the art and each process may have aprocessing module or tool associated therewith. For example, referringto FIG. 1 the method may include a film-forming or deposition process110 to form one or more layers on the workpiece. The layer may then becoated with a light sensitive material in a track process 112 beforebeing exposed to a patterned wavelength of light using aphotolithography process 114. The light sensitive material is thendeveloped using another track process 116 to form a pattern in thelight-sensitive material which exposes the underlying workpiece or film.Next, the exposed pattern may be used as a template to remove exposedportions of the underlying workpiece or film which are removed in apattern by using a removal or etch process 118. In this way, the patternexposed from the photolithography process 114 is transferred to theworkpiece or to one or more of the films overlaying the workpiece. Insome instances, the workpiece may be cleaned, using a cleaning process120, to remove the light sensitive material or clean the newly patternedfeatures in preparation for subsequent processing.

For film-forming or deposition processes, the term “film-forming” willgenerally be used herein for consistency. For film removal, the term“etch” will be used and for a cleaning removal process, the term “clean”will be used. The figures may use other designations as applicable forillustrative clarity or convenience.

As depicted, the example fabrication process 100 represents thefabrication of a single layer on a semiconductor workpiece. Arrow 130indicates that the fabrication process involves multiple passes throughthe processing steps in a sequence that results in the multiple stackingof layers of patterns to form devices on the substrate. While thefabrication of a single layer is described in a particular order herein,it is not uncommon for some steps to be skipped and other steps repeatedduring the fabrication of a single layer. Furthermore, more steps thanfilm-forming, etch, and clean may be utilized as would be understood bya person of ordinary skill in the art. Still further, each of the stepsof a film-forming or etch process may include various specific steps.Therefore, the exemplary illustrative process of FIG. 1 is not limitingwith respect to the present invention.

For example, the noted deposition process 110 employs a depositionmodule/tool that grows, coats, or otherwise forms or transfers amaterial film onto the workpiece. A deposition process may employ one ormore technologies and methods to accomplish this task. Examples offilm-forming or deposition technologies include physical vapordeposition (PVD), chemical vapor deposition (CVD), electrochemicaldeposition (ECD), molecular beam epitaxy (MBE), atomic layer deposition(ALD), self-assembled monolayer (SAM) deposition and others. Moreover,these deposition techniques may be complemented or enhanced by thecreation of plasma to affect the chemical reactivity of the processesoccurring at the substrate surface.

The photolithography process 114 employs a photolithographic module/toolthat is used to transfer a pattern from a photomask to the surface ofthe workpiece. The pattern information is recorded on a layer ofphotoresist which is applied on the workpiece. The photoresist changesits physical properties when exposed to light (often ultraviolet) oranother source of illumination (e.g., X-ray). The photoresist is eitherdeveloped by (wet or dry) etching or by conversion to volatile compoundsthrough the exposure itself. The pattern defined by the mask is eitherremoved or remains after development, depending on whether the type ofresist is positive or negative. For example, the developed photoresistcan act as an etching mask for the underlying layers.

Typically, the track process 112 includes using a track module/toolwhich prepares the workpiece for the photolithography process orexposure. This may involve cleaning of the workpiece or add a coating orfilm thereon. The coating may include a light-sensitive material,typically referred to as photoresist that is altered by the lightexposed through a mask in the photolithography process 114. Similarly,the track process 116 may use a tool that handles the workpiece afterthe photolithography process 114, typically developing the photoresistto form the pattern that may expose portions of the underlyingworkpiece. Often, this involves post-lithographic cleaning orpreparation for the next process step in the fabrication.

The etch process 118 includes an etching module/tool that is used toremove material selectively on the surface of the workpiece in order tocreate patterns thereon. Typically, the material is selectively removedeither by wet etching (i.e., chemical) or dry etching (i.e., chemicaland/or physical). An example of dry etching includes, but is not limitedto, plasma etching. Plasma etching involves forming plasma of anappropriate gas mixture (depending on the type of film being etched)that is exposed to the workpiece. The plasma includes charged (ions andfree electrons) and neutral (molecules, atoms, and radicals) species inthe gas-phase that kinetically interact with the substrate or layer toremove portions of the substrate or layer, particularly the portionsexposed by an overlying photolithography pattern.

The clean process 120 may include a cleaning module/tool that is used toclean the workpiece (e.g., remove photoresist) and/or prepare theworkpiece for the application or deposition of the next layer.Typically, the cleaning process removes particles and impurities on theworkpiece and can be a dry clean process or a wet clean process.

In accordance with one embodiment the invention, fabrication measurementor metrology data is captured after one or more of the various substratefabrication processes as shown in FIG. 1. As used herein, the captureddata from a workpiece is referred to as measurement data or metrologydata. The measurement data is captured utilizing one or more measurementmodules or metrology modules that can be incorporated within separatemetrology chambers on a common manufacturing platform as discussedherein or using measurement module/metrology module incorporated withina workpiece transfer module that moves workpiece between one or more ofthe processing modules that perform the various steps as set forth inFIG. 1. In accordance with one feature of the invention, the substrateis maintained in a controlled environment, such as under vacuum, duringthe capture of the measurement/metrology data. A measurement/metrologymodule/tool as utilized within a manufacturing platform such as shown inFIG. 2 is designed to measure data associated with an attribute of aworkpiece or attributes regarding features of a workpiece to measuresomething otherwise measurable such as, for example, the material layersthereon, the patterns imparted thereon, or dimensions and alignment forthe various devices fabricated on the substrate, for example. Themeasurement process, as performed by a measurement module/tool, may beimplemented with one or more of a plurality of workpiece processingsteps performed on a common manufacturing platform. Furthermore, ametrology measurement module or tool might be employed at various timeswithin a process and/or at multiple locations within a commonmanufacturing platform based upon where data is desired to improve orcorrect the process. For example, the location of a measurement modulemight be located within a platform proximate to certain processingmodules or following certain processes that might be prone to error inorder to quickly assess the specifications regarding one or more layersand the attributes of features being fabricated on a workpiece.

In accordance with one embodiment of the invention, a semiconductormanufacturing platform for the processing of a workpiece and for thefabrication of electronic devices includes a plurality of processingmodules that are hosted on a common manufacturing platform. Theprocessing modules are configured for facilitating different processesand manipulating materials on a workpiece in a plurality of processingsteps according to a defined processing sequence. More specifically, theprocessing modules may include one or more film-forming modules fordeposing material layers on a workpiece and one or more etch modules forselectively removing material layers. Other modules such as cleaning ortracking or photo-lithography modules may also be included in the commonplatform. As used herein, the term “processing module” or “module” isused to refer to a processing system that will generally include one ormore processing chambers that will contain one or more workpieces andalso the supporting and surrounding infrastructure and components forthe processing, such as gas supplies, dispense systems, RF (radiofrequency) power supplies, DC (direct current) voltage supplies, biasingpower supplies, substrate supports, substrate clamping mechanisms,substrate and chamber component temperature control elements, etc.

On the common platform, one or more metrology or measurement modules ishosted with the processing modules. The measurement module is configuredto provide measurement data associated with one or a plurality ofattributes of a workpiece. To that end, the measurement modules includesone or more inspection systems that are operable for measuring dataassociated with an attribute of the workpiece. Generally, themeasurement modules will be positioned and arranged in the commonplatform and with the processing modules to make measurements beforeand/or after the workpiece is processed in a processing module in theplatform.

As disclosed herein the term “metrology module” or “measurement module”refers to a module/system/sensor/tool that can make measurements on aworkpiece to detect or determine various non-conformities or variationson the workpiece, such as parametric variations, or to detect ordetermine defects on the workpiece, such as a contamination of somekind. As used herein, the term “inspection system” will generally referto the tool or system of a measurement process or module that measuresand collects data or signals associated with the measurement. Themeasurement modules will make measurements and provide data for use inthe processing platform as disclosed further herein. For consistencyherein, the term “measurement module” will be used but that is notlimiting and generally refers to measurement or metrology or sensingtools used to detect and measure attributes of a workpiece that areindicative of the processing of the workpiece and the layers and devicesbeing formed thereon.

To move workpieces in a platform and between the various processingmodules, the common manufacturing platform will generally incorporateone or more workpiece transfer modules that are hosted on the commonplatform and are configured for the movement of the workpiece betweenthe processing modules and the measurement module(s). A measurementmodule might be coupled with the workpiece transfer module similar to aprocessing module. In some embodiments of the invention, as disclosedherein, a measurement module or the inspection system associatedtherewith is incorporated with or inside a transfer module to providefor measurement or metrology as the workpiece is moved betweenprocessing modules. For example, a measurement module, or a portionthereof, might be positioned inside an internal space of the transfermodule. Herein, the combination transfer and measurement apparatus willbe referred to as a transfer measurement module.

In one embodiment of the invention, the common platform including bothprocessing chambers and measurement modules is actively controlled by asystem that processes the measured data associated with an attribute onthe workpiece and uses the measured data for controlling movement andprocessing of the workpiece in a processing sequence. In accordance withthe invention, the control system uses measured data and other data toperform corrective processing based in part on the measured data, toprovide active interdiction of the processing sequence to correctnon-conformities or defects. More specifically, an active interdictioncontrol system is hosted on the common manufacturing platform and isconfigured to perform corrective processing based in part on themeasured data, wherein the corrective processing of the workpiece mightbe performed in the processing modules of the platform that are upstreamor downstream in the process sequence to address situations wherenon-conformities or defects are detected. In an embodiment of theinvention, the workpiece is maintained in a controlled environment, suchas under vacuum, for example. That is, on the common manufacturingplatform, the processing modules and the measurement module operate in acontrolled environment, and the workpiece transfer module transfers theworkpiece between the plurality of processing modules in the processingsequence and one or more measurement modules without leaving thecontrolled environment.

FIGS. 2 and 3 illustrate exemplary systems 200, 300 that incorporate acommon platform with multiple processing modules, one or moremeasurement modules and one or more transfer modules coupled with anactive interdiction control system. The systems enhance the yield offunctional microelectronic devices produced from semiconductorfabrication in accordance with the invention as described herein. FIG. 2diagrammatically illustrates an exemplary system 200 that facilitatesthe measurement of metrology data and use of the data for theamelioration or correction of systemic layer or feature non-conformitiesor defects during semiconductor fabrication in accordance with theinvention as described herein. The exemplary system 200 includes variousprocess modules to perform the various processes of the semiconductorfabrication method 100 described above and shown in FIG. 1. In FIG. 2,the various processes are reflected by different noted modules thatperform a task or process related to the fabrication along withmeasurement modules and transfer modules under the control of an activeinterdiction system.

As depicted, the system of common platform 200 shows the interaction ofthe platform rather than a particular physical layout. Platform 200includes one or more processing modules for the various processes of asemiconductor fabrication process such as deposition modules 210, etchmodules 218, clean modules 220, track modules 212, 216, andphotolithography modules 214. As may be appreciated, one or more modulesmight be incorporated into a common platform in various ways andtherefore, the figures are schematic rather than indicative of how theelements/modules are incorporated onto a platform. The system ofplatform 200 further includes one or more metrology or measurementmodules 202, 204, 206 for capturing measurement data as well as anactive interdiction control system 208 using the captured measurementdata to perform corrective processing based at least in part on themeasured data for improving the fabrication process. The activeinterdiction control system is coupled with the various measurementmodules and will process measured data associated with attributes on theworkpiece and use the measured data to detect non-conformities on aworkpiece. The active interdiction control system then controls movementand processing of the workpiece to provide correction in the processingsequence or “corrective processing.”

The metrology technologies described herein may be incorporated withonly one part/portion of the exemplary platforms 200, 300, or withmultiple parts/portions of the exemplary platforms 200, 300. That is,the technologies described here may, for example, be incorporated aroundonly one process or one process tool (e.g., the etch module 218).Alternatively, for example, active interdiction technologies describedherein may be implemented for multiple processes and tools and systemsin the process platforms 200, 300. For example, the correctiveprocessing is performed, at least in part via the operation of one ormore processing modules upstream or downstream in the process sequence.

As used herein, the term “active interdiction” refers generally to thecontrol system as implemented for capturing measurement/metrology datain real time with respect to various fabrication processes to obtaindata on workpiece attributes and thereby detect non-conformities ordefects and the corrective aspects of the control to correct orameliorate the non-conformities or defects. The active interdictioncontrol system uses the data for correction and amelioration of variousnon-conformities in the semiconductor fabrication process by activelyvarying the processing sequence and/or the operation of modules thatperform process steps. Thus, the active interdiction control system alsointerfaces with one or more transfer modules 222 used to move workpiecesthrough the process. The active interdiction control system 208 as shownin FIGS. 2 and 3 coordinates the data collection and data analysis anddetection of non-conformities with the fabrication process and furtherdirects the actions of multiple processing tools and processing chambersso as to address the non-conformities or defects that are detected. Theactive interdiction control system is implemented generally by one ormore computer or computing devices as described herein that operate aspecially designed sets of programs such as deep learning programs orautonomous learning components referred to collectively herein as activeinterdiction components. As may be appreciated, the active interdictioncontrol system may incorporate multiple programs/components tocoordinate the data collection from various measurement modules and thesubsequent analysis. The system 208 interfaces with the multipleprocessing modules in a manufacturing platform in order to addressvarious measured non-conformities/defects to correct or ameliorate thenon-conformities/defects. The active interdiction control system willthereby control one or more of the processing modules and the processingsequence to achieve the desired results of the invention.

The present invention also incorporates one or more transfer modules 222within the common platform for transferring workpieces between thevarious processing modules according to the defined processing sequence.To that end, the active interdiction control system also controls thetransfer modules in order to move the workpieces to upstream and/ordownstream processing modules when non-conformities/defects aredetected. That is, depending upon what is detected, the system of theinvention may move the work piece further along in the processingsequence, or may go back and direct the workpiece to an upstreamprocessing module to correct or otherwise address a detectednon-conformity or defect. As such, feedforward and feedback mechanismsare provided through the transfer modules to provide the activeinterdiction of the invention. Furthermore, the processing sequencemight be affected upstream or downstream for future workpieces.

The active interdiction features of the invention improve performance,yield, throughput, and flexibility of the manufacturing process usingrun-to-run, wafer-to-wafer, within the wafer and real-time processcontrol using collected measurement/metrology data. The measured data iscollected, in real time during the processing, without removing theworkpiece/substrate/wafer from the processing environment. In accordancewith one feature of the invention, in a common platform, the measurementdata may be captured while the substrate remains in a controlledenvironment, such as under vacuum, for example. That is, the workpiecetransfer module(s) are configured for transferring the workpiece betweenthe plurality of processing modules and the measurement modules withoutleaving the controlled environment. The active interdiction control canprovide a multivariate, model-based system that is developed inconjunction with feed-forward and feedback mechanisms to automaticallydetermine the optimal recipe for each workpiece based on both incomingworkpieces and module or tool state properties. The active interdictioncontrol system uses fabrication measurement data, process models andsophisticated control algorithms to provide dynamic fine-tuning ofintermediate process targets that enhance final device targets. Theinterdiction system enables scalable control solutions across a singlechamber, a process tool, multi-tools, a process module and multi-processmodules on a common manufacturing platform using similar buildingblocks, concepts, and algorithms as described herein.

FIG. 3 is a schematic diagram of another system for implementing anembodiment of the present invention on a common manufacturing platform.The platform 300 incorporates a plurality of processing modules/systemsfor performing integrated workpiece processing and workpiecemeasurement/metrology under the control of an active interdictioncontrol system according to embodiments of the invention. FIG. 3illustrates an embodiment of the invention wherein one or more substratemeasurement modules are coupled together with one or more workpieceprocessing modules through one or more transfer modules. In that way, inaccordance with features of the invention, an analysis may be made ofthe workpiece to provide the measurement data associated with anattribute of the workpiece, such as regarding material properties of theworkpiece and the various thin films, layers and features that areformed on the workpiece while the workpiece remains within theprocessing system and platform. As discussed herein, measurements andanalysis may be made immediately upon completion of processing steps,such as an etch or deposition step, and the measurement data gatheredmay be analyzed and then used within the common platform processingsystem to address any measurements or features that are out ofspecification or non-conformal or represent a defect with respect to theworkpiece design parameters. The workpiece does not need to be removedfrom the common processing or manufacturing platform and if desired, canremain under the controlled environment.

Referring to FIG. 3, a common manufacturing platform 300 in accordancewith the invention is diagrammatically illustrated. Platform 300includes a front end module 302 for introducing one or more workpiecesinto the manufacturing platform. As is known, the front end module (FEM)may incorporate one or more cassettes holding the workpieces. The frontend module may be maintained at atmospheric pressure but purged with aninert gas to provide a clean environment. One or more of the substratesmay then be transferred into a transfer module 304 a, such as throughone or more load lock chambers (not shown) as discussed herein. Thetransfer modules of FIG. 3 are transfer measurement modules (TMM) thatinclude measurement tools or inspection systems integrated therein forcapturing data from a workpiece. Multiple TMM's, 304 a, 304 b may beinterfaced for providing movement of a workpiece through a desiredsequence. The transfer measurement modules 304 a, 304 b are coupled witha plurality of processing modules. Such processing modules may providevarious different processing steps or functions and may include one ormore etch modules 306 a, 306 b, one or more deposition modules 308 a,308 b, one or more cleaning modules 310 a, 310 b, and one or moremeasurement modules 312 a, 312 b, 312 c, 312 d. In accordance withembodiments of the invention as disclosed further herein, measurementmodules may be accessed through the transfer modules 304 a, 304 b beforeor after each processing step. In one embodiment, the measurementmodules, such as 312 c, 312 d are located outside of the transfermodules 304 a, 304 b and are accessed to insert and receive workpiecessimilar to the various processing modules. Alternatively, measurementmodules or at least a portion thereof such as modules 312 a, 312 b maybe located in a respective transfer module. More specifically, all or aportion of a measurement module 312 a, 312 b is located in transfermodule 304 a, 304 b to define a measurement region where a workpiecemight be positioned for measurement during a transfer process. Themeasurement region is located in a dedicated area of the transfer moduleand is accessible by the transfer mechanism of a module for positioningthe workpiece. As noted, this makes the transfer module essentially atransfer measurement module (TMM) as discussed herein.

Generally, the transfer module defines a chamber therein that houses atransfer robot that is capable of moving substrates, under vacuum,through various gate valves and access or transfer ports into variousprocessing modules or measurement modules. By maintaining themeasurement modules on the common manufacturing platform 300, they arereadily accessed, such as between one or more of the processing steps toprovide the necessary measured analytical data on-the-fly that will beused to address any substrates out of specification or otherwisenon-conformal with the substrate design plans for a particular workpieceor to address detectable defects. In that way, real time data isprovided to allow a fabricator to recognize problems early in the systemso that remedial action may be taken in the current processing sequence,such as in a following processing step, in a previous processing step,and/or in a future processing step depending upon the captured data andthe detected non-conformities or defects. In that way, productivity andefficiency may be increased, process monitoring overhead may be reduced,and wasted product, in the form of rejected or ejected substrates may bereduced. This all provides a significant cost savings to a fabricator ordevice maker.

As noted, in one embodiment of the invention that incorporates theactive interdiction control system 322, one or more measurement modulesare hosted on a common platform with processing modules for providingmeasured data regarding an attribute of the workpiece. The data is usedby the active interdiction control system 322 for detectingnon-conformities and for performing corrective processing of theworkpiece when non-conformities are detected. The corrective processingis performed upstream and/or downstream in the process sequence whennon-conformities are detected. Referring to FIG. 4, an exemplaryprocessing system on a common platform 400 suitable for practicing theinvention is illustrated. The processing system 400 incorporatesmultiple modules and processing tools for the processing ofsemiconductor substrates for the fabrication of integrated circuits andother devices. The processing platform 400 incorporates one or moresubstrate metrology/measurement modules that are incorporated within thecommon manufacturing platform along with the processing modules. Forexample, the platform 400 may incorporate a plurality of substrateprocessing modules that are coupled to a workpiece transfer module asshown. In some embodiments, a measurement module or tool is alsopositioned, at least partially, inside the substrate transfer module. Assuch, a substrate may be processed and then transferred immediately to ameasurement module in order to collect various fabrication dataassociated with attributes of the workpiece that is further processed bythe active interdiction control system. The active interdiction controlsystem gathers data from the processing and measurement modules andcontrols a process sequence that is executed on the common manufacturingplatform through the selective movement of the workpiece and control ofone or more of the plurality of processing modules. Furthermore, theprocessing system of platform 400 may transfer a substrate or otherworkpiece inside the chamber of the transfer module and between thevarious processing modules and the measurement/metrology modules withoutleaving the controlled environment of the chamber. The activeinterdiction control system controls the sequential process flow throughthe various processing modules utilizing information that is derivedfrom workpiece measurements obtained from the one or more measurementmodules. Furthermore, the active interdiction control systemincorporates processing modules in-situ measurements and data to controlthe sequential process flow through the platform 400. The on-substratemeasurement data obtained in the controlled environment may be utilizedalone or in combination with the in-situ processing module measurementdata for process flow control and improvement of the process inaccordance with the invention.

Turning again to FIG. 4, the system of platform 400 contains a front endworkpiece transfer module 402 to introduce workpieces to the system. Theexemplary platform 400 represents a plurality of processing modulesorganized in a common manufacturing platform around the periphery ofworkpiece transfer module 412. The system of platform 400 includescassette modules 404 a, 404 b. and 404 c and an alignment module 404 d.Load-lock chambers 406 a and 406 b, are also coupled to a front endtransfer module 402. The front end module 402 is generally maintained atatmospheric pressure but a clean environment may be provided by purgingwith an inert gas. Load-lock chambers 410 a and 410 b are coupled to thecentralized workpiece transfer module 412 and may be used fortransferring substrates from the front end 402 to the workpiece transfermodule 412 for processing in the platform.

The workpiece transfer module 412 may be maintained at a very low basepressure (e.g., 5×10−8 Torr, or lower) or constantly purged with aninert gas. In accordance with the invention, a substratemeasurement/metrology module 416 may be operated under atmosphericpressure or operated under vacuum conditions. In accordance with oneembodiment, the measurement module 416 is kept at vacuum conditions andthe wafer is processed in platform 400 and measured without leavingvacuum. As disclosed further herein, the metrology module may includeone or more inspection systems or analytical tools that are capable ofmeasuring one or more material properties or attributes of a workpieceand/or of the thin films and layers deposited on the workpiece or thedevices formed on the workpiece. As used herein, the term “attribute” isused to indicate a measurable feature or property of a workpiece, layeron a workpiece, feature or device on a workpiece, etc. that isreflective of the processing quality of the processing sequence. Themeasured data associated with an attribute is then used to adjust theprocess sequence by analyzing the measured data along with other in-situprocessing data through the active interdiction control system. Forexample, the measured attribute data reflects non-conformities ordefects on the workpiece for providing corrective processing.

FIG. 4 and the platform illustrated therein shows essentially a singlemeasurement module 416. However, as will be understood and as disclosedfurther herein, the particular processing platform 400 may incorporate aplurality of such measurement modules that are incorporated around oneor more workpiece transfer systems, such as the workpiece transfermodule for 412. Such measurement modules 416 may be stand-alone modulesthat are accessed through the transfer module 412 like a processingmodule. Such stand-alone modules will generally incorporate inspectionsystems therein that are configured to engage a workpiece that ispositioned in a measurement region of the module and to measure dataassociated with an attribute of the workpiece.

In an alternative embodiment of the invention, a measurement modulemight be implemented in a measurement region located within a dedicatedarea of an internal space of the transfer chamber defined by thetransfer module 412. Still further, a measurement module might beincorporated wherein at least a portion of the measurement module ispositioned inside of an internal space of a workpiece transfer module,and other components of the measurement module or the specificinspection system of the measurement module are incorporated outside ofthe workpiece transfer module and interfaced through an aperture orwindow into a dedicated area of the internal space that forms themeasurement region in which a workpiece is located or through which aworkpiece will pass.

The measurement modules of the inventive system and platform include oneor more inspection systems that are operable for measuring dataassociated with an attribute of the workpiece. Such data may beassociated with one or more attributes that reflect the quality of theprocessing sequence and the quality of the layers and features anddevices that are being formed on a workpiece. The collected measurementdata is then analyzed, along with processing module data, by an activeinterdiction control system for detecting various non-conformitiesand/or defects on the workpiece or workpiece layers/features. The systemthen provides for corrective processing of the workpiece, such as inupstream or downstream processing modules in the process sequence toameliorate/correct the non-conformities or defects and improve theoverall process.

In accordance with embodiments of the invention, the measurements takenby the measurement module or inspection systems thereof and the datagenerated is associated with one or more attributes of a workpiece. Forexample, the attribute measured may include, for example, on or more of:a layer thickness, a layer conformality, a layer coverage, or a layerprofile of a layer on the workpiece, an edge placement location, an edgeplacement error (EPE) for certain features, a critical dimension (CD), ablock critical dimension (CD), a grid critical dimension (CD), a linewidth roughness (LWR), a line edge roughness (LER), a block LWR, a gridLWR, a property relating to selective deposition process(es), a propertyrelating to selective etch process(es), a physical property, an opticalproperty, an electrical property, a refractive index, a resistance, acurrent, a voltage, a temperature, a mass, a velocity, an acceleration,or some combination thereof associated with the fabricated electronicdevices on the workpiece. The list of measured attributes for generatingmeasurement data for the invention is not limited and could includeother attribute data that might be used for processing a workpiece andfabricating devices.

As further discussed herein, the measurement modules and/or inspectionssystems used for providing attribute data may implement a number oftools and methods for measurement for providing the measurement andmetrology of the invention. The measurement modules and/or inspectionssystems may include optical methods, or non-optical methods. Opticalmethods can include high-resolution optical imaging and microscopy(e.g., bright-field, dark-field, coherent/incoherent/partially coherent,polarized, Nomarski, etc.), hyperspectral (multi-spectral) imaging,interferometry (e.g., phase shifting, phase modulation, differentialinterference contrast, heterodyne, Fourier transform, frequencymodulation, etc.), spectroscopy (e.g., optical emission, lightabsorption, various wavelength ranges, various spectral resolutions,etc.), Fourier transform Infrared spectroscopy (FTIR) reflectometry,scatterometry, spectroscopic ellipsometry, polarimetry, refractometers,etc. Non-optical methods can include electronic methods (e.g., RF,microwave, etc.), acoustic methods, photo-acoustic methods, massspectroscopy, residual gas analyzers, scanning electron microscopy(SEM), transmission electron microscopy (TEM), atomic force microscopy(AFM), energy dispersive x-ray spectroscopy (EDS), x-ray photo-emissionspectroscopy (XPS), ion scattering, etc. For example, the inspectionsystem used for measuring data that is associated with an attribute ofthe workpiece may use one or more of the following techniques ordevices: optical thin film measurement, such as reflectometry,interferometry, scatterometry, profilometry, ellipsometry; X-Raymeasurements, such as X-ray photo-emission spectroscopy (XPS), X-Rayfluorescence (XRF), X-Ray diffraction (XRD), X-Ray reflectometry (XRR);ion scattering measurements, such as ion scattering spectroscopy, lowenergy ion scattering (LEIS) spectroscopy, auger electron spectroscopy,secondary ion mass spectroscopy, reflection absorption IR spectroscopy,electron beam inspection, particle inspection, particle counting devicesand inspection, optical inspection, dopant concentration metrology, filmresistivity metrology, such as a 4-point probe, eddy currentmeasurements; a micro-balance, an accelerometer measurement, a voltageprobe, a current probe, a temperature probe for thermal measurements, ora strain gauge. The list of measurement techniques or devices forgenerating measurement data for the invention is not limited and couldinclude other techniques or devices that might be used for obtaining theuseful data for processing a workpiece and fabricating devices inaccordance with the invention.

The measurement modules and/or inspection systems may take measurementson various substrate or workpiece structures passed through theprocessing system including either product workpieces, or non-productsubstrates, i.e., a monitoring substrate. On product workpieces,measurements can be performed on designated target structures, bothdevice-like structures and device-unlike structures, on specified deviceareas, or on arbitrary areas. The measurements may also be performed ontest structures created on the workpiece, that might include pitchstructures, area structures, density structures, etc.

Referring again to FIG. 4, coupled to the transfer chamber 412 are aplurality of processing modules 420 a-420 d that are configured forprocessing substrates, such as semiconductor or silicon (Si) workpieces.The Si workpieces can, for example, have a diameter of 150 mm, 200 mm,300 mm, 450 mm, or larger than 450 mm. The various processing modulesand measurement modules all interface with the workpiece transfer module412 through appropriate gate access ports with valves G, for example.According to one embodiment of the invention disclosed herein, the firstprocessing module 420 a might perform a treatment process on aworkpiece, and the second processing module 420 b might form aself-aligned monolayer (SAM) on a workpiece. The third processing module420 c may etch or clean a workpiece, and the fourth processing module420 d may deposit a film on a workpiece by a suitable depositionprocess.

The transfer module 412 is configured for transferring substratesbetween any of the substrate processing chambers 420 a-420 d and theninto the substrate metrology module 416 either before or after aparticular processing step. FIG. 4 further shows the gate valves G thatprovide isolation at the access ports between adjacent processingchambers/tool components. As depicted in the embodiment of FIG. 4, thesubstrate processing chambers 420 a-420 d and the substrate metrologymodule 416 may be directly coupled to the substrate transfer chamber 412by the gate valves G and such direct coupling can greatly improvesubstrate throughput in accordance with the invention.

The substrate processing system of platform 400 includes one or morecontrollers or control systems 422 that can be coupled to control thevarious processing modules and associated processing chambers/toolsdepicted in FIG. 4 during the integrated processing andmeasurement/metrology process as disclosed herein. Thecontroller/control system 422 can be coupled to one or more additionalcontrollers/computers/databases (not shown) as well. Control system 422can obtain setup and/or configuration information from an additionalcontroller/computer or a server over a network. The control system 422is used to configure and run any or all of the processing modules andprocessing tools and to gather data from the various measurement modulesand in-situ data from the processing modules to provide the activeinterdiction of the invention. The controller 422 collects, provides,processes, stores, and displays data from any or all of the processingmodules and tool components. The control system 422, as describedfurther herein, can comprise a number of different programs andapplications and processing engines to analyze the measured data andin-situ processing data and to implement algorithms, such as deeplearning networks, machine learning algorithms, autonomous learningalgorithms and other algorithms for providing the active interdiction ofthe invention.

As described further herein, the active interdiction control system 422can be implemented in one or more computer devices having amicroprocessor, suitable memory, and digital I/O port and is capable ofgenerating control signals and voltages that are sufficient tocommunicate, activate inputs to the various modules of the platform 400,and exchange information with the substrate processing systems run onthe platform 400. The control system 422 monitors outputs from theprocessing system of the platform 400 as well as measured data from thevarious measurement modules of the platform to run the platform. Forexample, a program stored in the memory of the control system 422 may beutilized to activate the inputs to the various processing systems andtransfer systems according to a process recipe or sequence in order toperform desired integrated workpiece processing.

The control system 422 also uses measured data as well as in-situprocessing data output by the processing modules to detectnon-conformities or defects in the workpiece and provide correctiveprocessing. As discussed herein, the control system 422 may beimplemented as a general purpose computer system that performs a portionor all of the microprocessor based processing steps of the invention inresponse to a processor executing one or more sequences of one or moreinstructions contained in a program in memory. Such instructions may beread into the control system memory from another computer readablemedium, such as a hard disk or a removable media drive. One or moreprocessors in a multi-processing arrangement may also be employed as thecontrol system microprocessor element to execute the sequences ofinstructions contained in memory. In alternative embodiments, hard-wiredcircuitry may be used in place of or in combination with softwareinstructions for implementing the invention. Thus, embodiments are notlimited to any specific combination of hardware circuitry and softwarefor executing the metrology driver processes of the invention asdiscussed herein.

The active interdiction control system 422 may be locally locatedrelative to the substrate processing system of platform 400, or it maybe remotely located relative to the substrate processing system. Forexample, the controller 422 may exchange data with the substrateprocessing system and platform 400 using at least one of a directconnection, an intranet connection, an Internet connection and awireless connection. The control system 422 may be coupled to anintranet at, for example, a customer site (i.e., a device maker, etc.),or it may be coupled to an intranet at, for example, a vendor site(i.e., an equipment manufacturer). Additionally, for example, thecontrol system 422 may be coupled to other systems or controls throughan appropriate wired or wireless connection. Furthermore, anothercomputer (i.e., controller, server, etc.) may access, for example, thecontrol system 422 to exchange data via at least one of a direct wiredconnection or a wireless connection, such as an intranet connection,and/or an Internet connection. As also would be appreciated by thoseskilled in the art, the control system 422 will exchange data with themodules of the substrate processing system 400 via appropriate wired orwireless connections. The processing modules may have their ownindividual control systems (not shown) that take input data for controlof the processing chambers and tools and sub-systems of the modules andprovide in-situ output data regarding the process parameters and metricsduring processing sequence.

FIGS. 5A-5D illustrate one embodiment of a common platform with on-boardmeasurement and metrology for implementing the invention. Similar to thesystem illustrated in FIG. 4, the substrate processing systemimplemented on platform 500 incorporates a front end transfer system orFEM 502 coupled with cassette modules 504 a, 504 b and load lockchambers 510 a, 510 b. A substrate transfer module 512 moves substratesbetween one or more processing modules 520 a, 520 b, 520 c, and 520 dand one or more measurement/metrology modules 516. Generally, thetransfer module 512 has a chamber that incorporates one or more transfermechanisms or robots 514 that will handle and move substrates throughthe internal space of the chamber and into and out of the processingmodule in the processing sequence.

More specifically, the transfer mechanism 514 is positioned inside ofthe internal space 513 of the transfer module that can define acontrolled environment and is configured for moving the workpiecesthrough the internal space and environment and selectively in and out ofthe plurality of processing modules 520 a-520 d and the measurementmodules 516 or into and out of a measurement region in a dedicated areaof the internal space in order for a measurement inspection system tomeasure data. In accordance with one feature of the invention, becausethe internal space 513 of the transfer module 512 and processing modules520 a-520 d and measurement modules 516 are coupled together on thecommon platform, the controlled environment may be maintained for theworkpiece generally through most of or all of the measurement andprocessing sequence. Such a controlled environment could involve avacuum environment or an inert gas atmosphere in the transfer module ormeasurement module.

Similar to the embodiment illustrated in FIG. 4, the system 500 in FIG.5A incorporates at least one workpiece measurement/metrology module 516that is coupled with the transfer module 514 through an appropriateaccess port and gate G similar to the various processing modules 520a-520 d.

More specifically, the transfer module 512 includes a plurality ofaccess ports or side ports, each with a suitable gate G, through which aworkpiece is moved to and from the plurality of processing modules 520a-520 d. To provide the necessary processing sequence for efficientthrough-put on platform 500, the plurality of processing modules 520a-520 d includes modules that handle a variety of workpiece processingsteps on the common platform. For example, the platform will include oneor more etching modules and one or more film-forming or depositionmodules. The measurement module 516, as illustrated in FIG. 5A iscoupled with the transfer module also at one of the side or access portsthrough a suitable gate G. In other embodiments, as illustrated in FIG.6A, the measurement module is coupled with the transfer module at a portformed in the top of the transfer module. In still further embodimentsas described herein, the transfer module acts as a measurement module aswell wherein at least a portion of the measurement module for capturingmeasurement data is incorporated or positioned inside of an internalspace of the transfer module. The transfer measurement module (TMM) insuch an embodiment, as illustrated in FIGS. 7A-7C, includes ameasurement region located within a dedicated area of the internal spaceof the transfer module.

The active interdiction control system collects workpiece measurementdata generally on-the-fly as the substrate moves in the processingsequence between one or more of the processing modules and themeasurement/metrology module 516. The data is captured and then analyzedand processed to detect non-conformities and defects and providecorrective processing as discussed herein. The active interdictioncontrol system 522 provides the necessary control of the processingsteps of the sequence to make control adjustments to various fabricationprocessing steps as performed in order to correct for the detectednon-conformities/defects. Adjustments may be made to process steps andprocessing chambers that precede or are upstream of the capturedmeasurement data and/or process steps that follow or are downstream ofthe measurement data in sequence. Alternatively, a suitable correctiveaction or corrective processing might include ejection of theworkpiece(s) from the processing flow-through platform 500 in order tonot waste further time and materials on a workpiece(s) which cannot besaved.

Referring to FIG. 5B, one exemplary measurement module 516 isillustrated that incorporates an inspection system 530 for makingmeasurements on the substrate, in real-time with respect to theprocessing sequence through the system of common platform 500.

The inspection system 530 measures data associated with an attribute ofthe workpiece that may include data associated with one or moreproperties, such as a physical property, a chemical property, an opticalproperty, an electrical property, a material property or somecombination of two or more thereof. The measurement data may alsoinclude data associated with one or more layers formed on the workpiece.As noted, the inspection system or tools used for measuring data in themeasurement module may use various different techniques involving signalsources and signal capture sensors, contact sensors, and othermeasurement tools to implement one or more of the following techniquesor devices: optical thin film measurement, such as reflectometry,interferometry, scatterometry, profilometry, ellipsometry; X-Raymeasurements, such as X-ray photo-emission spectroscopy (XPS), X-Rayfluorescence (XRF), X-Ray diffraction (XRD), X-Ray reflectometry (XRR);ion scattering measurements, such as ion scattering spectroscopy, lowenergy ion scattering (LEIS) spectroscopy, auger electron spectroscopy,secondary ion mass spectroscopy, reflection absorption IR spectroscopy,electron beam inspection, particle inspection, particle counting devicesand inspection, optical inspection, dopant concentration metrology, filmresistivity metrology, such as a 4-point probe, eddy currentmeasurements; a micro-balance, an accelerometer measurement, a voltageprobe, a current probe, a temperature probe for thermal measurements, ora strain gauge. As the workpiece is moved through the processingsequence and through a metrology module or TMM, the inspection systemmeasures data before or after the workpiece is processed in a processingmodule to determine the operation of the processing step and module andto evaluate any need for corrective processing in accordance with theinvention.

In the illustrated embodiment of FIG. 5B, the inspection system 530incorporates one or more signal sources 532 which direct a measurementsignal 534 toward a workpiece 536. Incident signals 534 are reflected orscattered from the surface of the workpiece 536 and the scatteredsignals 535 are captured by the detector 540. In one embodiment, theworkpiece is positioned by transfer mechanism 514 on a measurementplatform 538 that may be translated side-to-side and up and down androtated as indicated by the arrows in FIG. 5B so that a measurementsignal 534 may be directed to various proper positions on the substrate536.

That is, in the embodiment of FIG. 5B, the measurement module includes aseparate support mechanism 538 for supporting a workpiece positioned inthe measurement module 516. The inspection system engages the supportmechanism 538 for measuring data associated with a workpiece attributesupported on the support mechanism. In such a scenario, the supportmechanism 538 in the measurement module 516 is generally separate fromthe transfer mechanism that otherwise moves the workpiece and positionsit on the support mechanism.

The separate support mechanism translates the workpiece, such as throughvertical and/or horizontal movement and also may rotate the workpiece toprovide at least two degrees of freedom for measuring data associatedwith an attribute of the workpiece as discussed herein. The supportmechanism may also incorporate a temperature control element therein forcontrolling workpiece temperature. Therefore, in the embodiment of FIG.5B, the support mechanism provides the support and movement of theworkpiece necessary for the measurement of data after the workpiece ispositioned thereon by the transfer mechanism. In an alternativeembodiment of the invention, as shown in FIG. 5C, the transfer mechanismprovides the function of supporting and moving the workpiece forengagement with the inspection system for measuring data associated withan attribute on the workpiece.

Referring to FIG. 5C, the transfer mechanism will position the workpieceeither in the measurement module or in the case of a transfermeasurement module, in a measurement region located within a dedicatedarea of a transfer chamber, so that the inspection system may engage theworkpiece to obtain measurement data. That is, the transfer mechanismacts as, or includes, a suitable support mechanism for supporting theworkpiece and providing the necessary translation and/or rotation formeasurements associated with an attribute of the workpiece.

The support mechanism or transfer mechanism acting as a supportmechanism may incorporate a clamping mechanism (as illustrated andincorporated herein by reference). Also, the support mechanism ortransfer mechanism providing the workpiece support mechanism might alsoincorporate a magnetically levitated stage for providing one or moredegrees of freedom, as disclosed herein.

The inspection system 530 includes one or more inspection signal sources532 and one or more signal collectors or signal detectors 540 to capturereflected or scattered signals from the surface of the workpiece 536that is being measured. The detectors 540 generate measurement data 550which may then be directed to the active interdiction control system 522as described herein.

Referring again to FIG. 5B, a workpiece transfer mechanism or robot 514moves the substrate from a processing chamber 520 a-520 d into themeasurement module 516 for placement on a support mechanism platform538, or in the embodiment of FIG. 5C for positioning the workpiece toengage the inspection system. The inspection system 530 measures andcaptures measurement data. In one embodiment of the invention, themeasurement module 516 operates in a controlled, but non-vacuumenvironment. Alternatively, measurement module 516 provides a vacuumenvironment for the measurement. To that end, a gate valve 552 may beincorporated at the access port between the substrate transfer chamber512 and measurement module 516. As will be appreciated, if vacuum isnecessary within measurement module 516, appropriate vacuum equipment(not shown) may be coupled with interior space of module 516 for thatpurpose. Once the workpiece 536 is measured, it can be moved out ofmeasurement module 516 by the transfer mechanism 514 of the transferchamber 512 and then directed to one or more of the other processingchambers 520 a-520 d in accordance with the process flow, for example,after the data has been analyzed by the active interdiction controlsystem and an appropriate action, such as a corrective processingaction, has been determined.

As described further herein, the captured measurement data 550 may thenbe directed to control system 522 and further evaluated and analyzed todetermine a particular action for the substrate measured. If themeasurement data indicates that the measured parameters are withinspecification of the desired design and fabrication process, and/orthere are no actionable detected defects, the workpiece may proceed asnormal through the process flow within the system of platform 500.Alternatively, if the measured data 550 indicates that the workpiece isbeyond correction or amelioration, the workpiece might be ejected fromfurther processing. Alternatively, in accordance with an embodiment ofthe invention, the active interdiction control system may analyze thedata and provide corrective processing as one or more corrective stepsto be taken for that workpiece or to be made in various process steps ofthe overall process flow in order to correct the current workpiece, andalso to prevent the need for corrective action in other workpieces thatare subsequently processed in the system. Specifically, referring toFIG. 5B, the active interdiction control system may incorporate one ormore processing steps and processing components therein for yieldingcorrection to the process flow. First, the necessary measurement data550 may be captured and pre-processed as illustrated by block 554. Next,modeling and data analysis occurs on the captured data as well as anyin-situ processing data associated with one or more of the processingmodules and process steps as indicated by block 556. The modeling andanalysis may utilize artificial intelligence, including deep learningand autonomous learning programs and components as discussed furtherherein. Next, the analysis may provide corrective process control forthe system of platform 500 wherein one or more of the processing stepsand processing chambers are controlled to correct or ameliorateperceived or detected non-conformities or defects in the layers andfeatures that are out of specification with respect to the overalldesign for the substrate fabrication. The corrective process control ofblock 558 may be provided to one or more of the processing steps orprocessing modules and it may be applied to one or more processing stepsthat are previous in time (upstream) to the capture of the measurementdata 550 or may be applied to one or more of the process steps to follow(downstream) the capture of the measurement data 550 within the overallsubstrate fabrication according to the desirable design. As discussedherein, the active interdiction control system 522, and its processes asindicated by blocks 554, 556 and 558 may be incorporated in software runby one or more computers of the control system 522 and/or components ofthat system.

In accordance with embodiments of the invention, the inspection systemsfor obtaining measurement data engage the workpiece by performingcontact measurement or metrology or non-contact measurement or metrologydepending on the attribute measured or the type of measurement. Acombination of both contact and non-contact measurement might be used.Depending on the location of the inspection system, portions of theinspection system may be positioned partially or entirely inside aninternal space or chamber of a module. In the embodiments of FIGS. 5Aand 6A as disclosed herein, dedicated measurement modules 516, 616 mayentirely contain the inspection system. Alternatively, a portion of ameasurement module might be positioned inside of an internal space of achamber, such as inside an internal space of a workpiece transfermodule, with another portion of the measurement module located outsideof the chamber. Such an embodiment is illustrated in FIG. 7A for examplewherein a transfer measurement module is illustrated using a measurementregion located within a dedicated area of the transfer chamber internalspace and the inspection system is configured for engaging a workpiecepositioned in the measurement region for measuring data associated withan attribute on the workpiece.

Referring now to FIG. 5E, the inspection system 530 may incorporate oneor more inspection signal sources 532 a, 532 b, 532 c that are utilizedin conjunction with one or more detectors 540 a, 540 b and 540 c tosense or collect inspection signals that are reflected or otherwisedirected from the surface of the workpiece 536 as it is moved within themeasurement module 516 or a transfer measurement module (TMM) to engagethe inspection system. In embodiments of the invention, the inspectionsystem 530 incorporates one or more signal sources 532 a-532 c togenerate and direct signals onto the surface of workpiece 536 that ispositioned and/or moved on a support mechanism 538 or on transfermechanism 514.

In accordance with embodiments of the invention, the signal sources 532a, 532 b, 532 c may generate one or more of an electromagnetic signal,an optical signal, a particle beam or a charged particle beam, or othersignal, to be incident upon a surface 539 of workpiece 538. Conversely,the detector elements 540 a, 540 b, 540 c, may be arranged to receivereflected or scattered corresponding electromagnetic signals, opticalsignals, particle beams or charged particle beams, or other signals thatmight be reflected or otherwise directed from surface 539 of theworkpiece 538 in order to measure data and provide metrology regardingan attribute of the workpiece.

Referring to FIG. 5E, support mechanism 538 or transfer mechanism 514holding workpiece 536 may be translated and rotated to providemeasurements of various areas on the workpiece 536. In that way,measurement data may be captured at various portions or segments of theentire workpiece. Thus, continuous measurements or point-by-pointmeasurements are possible thereby reducing the overall measurement timeand processing time.

For example, the inspection system measures data over a portion of theworkpiece that is equal to or exceeding 1 square centimeter.Alternatively, the inspection system measures or images a substantiveportion of the workpiece that is equal to or exceeding 90% of theworking surface area of the workpiece. As noted, the inspection systemmay perform a measurement at plural discrete locations on the workingsurface of the workpiece or may perform a continuous sequence ofmeasurements across a portion of the workpiece. For example, theinspection system may perform a measurement along a path extendingacross or partially across the workpiece. Such a path may include aline, a sequence of lines, an arc, a circular curve, a spiral curve, anArchimedean spiral, a logarithmic spiral, a golden spiral, or somecombination thereof. Also, there may be several inspection systems, asillustrated in FIG. 5C wherein the source/detector pairs 532, 540 mayeach represent a different inspection signal from a different inspectionsystem and may be different forms of signals. For example, one system532 a, 540 a might use an optical signal while one or more of the others532 ab 540 b might use an electromagnetic signal depending on theinspection system.

The inspection system(s) as shown in FIG. 5E perform multiplemeasurements of attributes on a workpiece while the workpiece is in ameasurement module or in dedicated area of a transfer measurement moduleas discussed herein. The measurements may be made simultaneously intime. That is, different inspection systems might make measurements atthe same time. Alternatively, the various inspection systems mightoperate at different times. For example, it may be necessary to move orposition the workpiece in one position for one type of measurement orinspection system, and then move or position the workpiece for anothermeasurement by the same or a different type of inspection system.

The inspection system(s) may be non-contact systems for providingnon-contact measurement and metrology, such as shown with the signalsources 532 a, 532 b, 532 c that generate the non-contact signals forthe detector elements 540 a, 540 b, 540 c. Alternatively, one or moreinspection systems of a measurement module or transfer measurementmodule might use a contact sensor, such as sensor 541 that may be movedand located by mechanism 543 to position the sensor 541 at a portion ofa surface 539 of the workpiece to make a measurement. The inspectionsystems provided in accordance with the invention may incorporate acombination of contact inspection systems and non-contact inspectionsystems for gather measurement data associated with an attribute of theworkpiece.

The surface 539 of the workpiece, as illustrated in FIG. 5E, that ismeasured with the inspection systems of a measurement module or transfermeasurement module as discussed herein will generally measure attributesassociated with the top surface or work surface of the workpiece.However, as discussed and further illustrated herein, the inspectionsystems might be arranged and positioned to make measurements and gatherdata from a bottom surface of the workpiece, if desired.

While the workpiece 536 measured will often be a workpiece to befinished into semiconductor devices, the measurements and metrology ofthe invention can be performed on either such product workpieces, ornon-product workpieces or substrates, i.e., a monitoring workpiece orsubstrate. On product workpieces substrates, measurement and metrologycan be performed on designated target structures, both device-like anddevice-unlike, in or on specified device areas, in or on arbitraryareas, or in or on test structures that are created on the workpiece.Test structures can include pitch structures, area structures, densitystructures, etc.

Generally, as illustrated in several figures, the inspection system asimplemented in a measurement module or in a transfer measurement moduleas disclosed herein may be stationary while the support mechanism orworkpiece transfer mechanism moves the workpiece to engage with theinspection system and to take measurements in different areas of theworkpiece. Alternatively, as illustrated in FIG. 5D, the inspectionsystem 530, or some portion thereof is movable with respect to theworkpiece support mechanism 538, the workpiece transfer mechanism 514and the module or chamber containing the workpiece, whether a chamber ofa measurement module or transfer measurement module. As illustratedshown in FIG. 5D, the inspection system might be configured to translateand or rotate with respect to the stationary workpiece to obtainmeasurement data from areas of the workpiece.

In other embodiments of the invention, the inspection system may beembedded in or part of a workpiece support mechanism. Referring to FIG.5F, the inspection system 530 might be mounted or supported on thesupport mechanism 538. Then, when the workpiece is positioned on thesupport mechanism, it will be in a proper position for engagement by theinspection system. Also shown in FIG. 5F, an inspection system 531,might be embedded in the support mechanism so as to sit below orotherwise proximate to a positioned workpiece. Such an inspection systemmight provide measurement data associated with a mass measurement or atemperature measurement of the workpiece, for example.

As discussed further herein, the inspection system 530 may be locatedwithin the measurement module, or transfer measurement module and thusmight operate to provide measurement data in a vacuum or controlledenvironment. Alternatively, the inspection system may incorporateinspection signal sources 532 and detectors 540 that are outside of thechamber or internal space that defines the measurement module. In suchcases, the signals may generally be directed through one or moreapertures, irises, or windows and into a space defined by the metrologymodule as discussed herein with respect to a transfer measurement moduleas illustrated in FIG. 7A.

FIGS. 6A and 6B illustrate alternative embodiments of the inventionwherein the measurement/metrology module is coupled with a plurality ofsubstrate processing chambers through a substrate transfer chamber, suchas in common platform 600. In the embodiment as illustrated in FIGS. 6Aand 6B, various elements noted are similar to those elements disclosedin FIG. 5A and so some of the similar reference numbers are maintainedfor such similar elements. More specifically, the measurement moduleand/or inspection systems as described herein may be similarlyimplemented and operated as discussed with platform 500 and the module516 of FIG. 5A.

In the system of common manufacturing platform 600 as illustrated inFIG. 6A, a measurement/metrology module 616 is implemented as a separatemodule. However, the module is positioned on top of the transfer module612 and has access through the top of the transfer module or through atop wall of the internal space of a transfer chamber 613 of the module612. As illustrated FIG. 6A, this provides additional space andlocations for additional processing modules, such as processing module620 e, positioned around the substrate transfer chamber 612.

Referring to FIG. 6B, the measurement/metrology module 616 as shown islocated on top of the transfer chamber 612. Accordingly, themeasurement/metrology module 616 may be accessed through a bottom areaof the module 616 and essentially through a top wall of the transferchamber 612. To that end, an opening or port 652 on the top of thesubstrate transfer chamber 612 will coincide with an opening or port inthe bottom of the measurement/metrology module 616. For example, asillustrated in FIG. 6B, a gate valve might be utilized at that access652 port as indicated at the interface between the measurement/metrologymodule 616 and the transfer chamber 612. The gate valve may be optionaldepending upon whether a vacuum is to be maintained within themeasurement/metrology module 616.

A support mechanism 638 for supporting workpiece 636 thereon willinclude an elevation mechanism 639 for raising and lowering the supportmechanism 638 as illustrated in FIG. 6B. In the lower position, as shownin dashed lines, the mechanism 638 is in position to receive a workpiece636 from the transfer mechanism or robot 614. Then the mechanism 639raises the support mechanism 638 into the chamber defined by themeasurement module 616 for engagement by one or more inspection systems630. While FIG. 6B discloses a single non-contact inspection system 630,other contact and non-contact inspection systems, as discussed withrespect to FIG. 5E and related figures, might be utilized with respectto the measurement module 616 in platform 500. The support mechanism 638and inspection system 630 may operate as discussed herein with respectto platform 500 and would have all of the features as noted with respectto that platform. Furthermore, although a single measurement module 616is illustrated, it will be appreciated that other measurement modulesand inspection systems might be implemented onto the top surface of thetransfer module 612 on the common platform 600.

As described herein, the inspection signal source 632 sends one or moreinspection signals 634 to the surface of workpiece 636 and those signalsare then reflected or scattered as indicated by signals 635 to bereceived by appropriate detectors 640. Thereby, measurement/metrologydata 550 is generated and may be appropriately processed as describedherein by active interdiction control system 522 which captures data,models and analyzes the data, and then provides corrective processcontrol for the system in platform 600. The control system affects theprocess flow and corrects or ameliorates any measurements which indicatethat non-conformities or defects or indicate that certain layers,features or devices are out of specification for the fabrication design.As may be appreciated, the embodiments illustrated in FIGS. 6A and 6Bprovide the ability to host a plurality of different processing moduleson a common manufacturing platform with one or moremeasurement/metrology modules wherein a workpiece being processed can bedirected immediately to the measurement/metrology module in a controlledenvironment or under vacuum to capture measurement/metrology data inreal time during a processing sequence and without removing thesubstrate from the controlled environment or vacuum environment.

While a common manufacturing platform may incorporate one or moremeasurement modules in combination with processing modules such as etchmodules and film-forming modules, in accordance with another embodimentof the invention, the functionality of a measurement/metrology module isincorporated within a transfer module that may move workpieces throughthe various processing modules according to a processing sequence. Morespecifically, the transfer module generally includes a transfer chamberdefining an internal space that holds a transfer mechanism, such as arobot, to move workpieces through the transfer module and into and outof selected processing modules. In accordance with the feature of theinvention, a measurement region is located within a dedicated area of atransfer chamber internal space. The measurement region is accessible bythe transfer mechanism for positioning a workpiece in the measurementregion for the purposes of obtaining measurement data. Morespecifically, a workpiece may be positioned in the measurement regionbefore or after the workpiece has been processed in a processing modulein order to determine the particular results of a processing step or theoverall processing sequence up to that point. An inspection system isconfigured to engage the workpiece that is positioned in the measurementregion. The inspection system is operable for measuring data that isassociated with an attribute on the workpiece in accordance withfeatures of the invention. As discussed further herein, the transfermechanism may place the substrate on a separate support mechanismlocated within the measurement region for taking the measurement.Alternatively, the transfer mechanism itself my act as a supportmechanism and move and position the workpiece in the appropriatemeasurement region for engagement by the inspection system. Accordingly,a separate measurement module is not necessary. Rather, the real estatewithin the transfer chamber of the transfer module provides access to aworkpiece for measurement.

FIG. 7A illustrates a processing system on a common platform 700incorporating a transfer module in accordance with one embodiment theinvention that utilizes a dedicated area to form a measurement regionwherein measurement data may be gathered from a workpiece duringtransit. In that way, as noted herein, the workpiece can be processedand measured while remaining within a controlled environment, such as avacuum environment. The workpiece does not need to leave the environmentof the platform 700 for determining how the process is proceeding andfor detecting any non-conformities or defects. Accordingly, theembodiment as illustrated in FIG. 7A forms a transfer measurement module(TMM) that may be utilized with one or more processing modules or aspart of a common platform. Furthermore, as discussed herein multipletransfer measurement modules may be utilized and interfaced together tocooperate and form a larger common manufacturing platform.

The inspection systems incorporated within a transfer measurement module(TMM) operate in and are similar to other inspection systems asdescribed herein. Such inspection systems as illustrated in FIGS. 7B and7C, for example, only illustrate certain inspection systems. However,other inspection systems and features, such as those discussed withrespect to FIGS. 5A-5F, would also be applicable to the transfermechanism module is illustrated in FIG. 7A. As such, some commonreference numerals are utilized in FIGS. 7A-7C as previously discussedherein.

The platform 700 incorporates a workpiece transfer module 712 thatprovides measurement/metrology data. The transfer measurement module(TMM) 712 includes a workpiece transfer mechanism, such as in the formof a handling robot 714 within the internal space of a transfer chamber713. The transfer mechanism 714 is operable as in platforms 500 and 600to move one or more or more workpieces through the transfer module 712and between various of the processing modules that are coupled totransfer chamber 712 in the common manufacturing platform is illustratedin FIG. 7A. In accordance with one feature of the invention, transferchamber 713 defines an internal space that includes a dedicated areathat is used for measurement. The measurement region 715 of the TMM 712is located in the dedicated area. The measurement region/area 715 isproximate to one or more inspection systems 730 for measurement.

More specifically, the measurement region 715 is positioned within thetransfer chamber 713 so as to not interfere with the primary purpose ofthe transfer measurement module in moving workpieces through the processsequence and into and out of various processing modules. The measurementregion defines one or more positions for placement of a workpiece formeasurement. To that end, one or more inspection systems are configuredto engage a workpiece that is positioned in the measurement region ofthe transfer chamber 713. The inspection system is then operable formeasuring data associated with an attribute on the workpiece inaccordance with the invention. As noted with the inspection systemsdisclosed herein, a support mechanism might be located within themeasurement region 715 for supporting a workpiece during the collectionof measurement data by the inspection system. Alternatively, thetransfer mechanism 714 may provide the positioning and support of theworkpiece within the measurement region 715 of the transfer chamber. Inaccordance with embodiments of the invention, the workpiece can be movedinto or through the measurement region 715 during a processing sequenceto obtain measurement data from one or more inspection systems that areassociated with that measurement region. While a single measurementregion is illustrated in FIG. 7A for illustrative purposes, multiplemeasurement regions 750 might be incorporated into the TMM 712.

Referring to FIG. 7B, the TMM module 712 incorporates one or moreinspection systems 730 located within a measurement region 715 andprovides the ability to obtain real-time measurements and measurementdata during a processing sequence. In one embodiment, measurement region715 within the TMM 712 incorporates a support mechanism 738 thatreceives a workpiece from mechanism 714 for measurement inside chamber713. Measurement data is captured as the workpiece is moved betweenprocessing modules.

Generally, the inspection system 730 in the TMM 712 is positionedproximate the measurement region and is configured for engaging aworkpiece in the measurement region 715 for measuring data associatedwith an attribute of the workpiece. As noted, the dedicated area fordefining the measurement region is located so that the workpiece supportmechanism and any associated inspection systems will not interfere withthe primary function of the TMM in moving workpieces in the processsequence and through one or more processing modules. The measurementmodule or inspection system that is part of the measurement module maybe entirely contained in the TMM to make measurements as shown in FIG.7C. In other embodiments a least a portion of the measurement module orinspection system is positioned inside of an internal space of the TMMso as to define a measurement region within a dedicated area of theinternal space as shown in FIG. 7B.

The inspection system 730 of a measurement module that is part of TMM712 may be a contactless system including one or more signal sources 732to generate inspection signals and one or more detectors 740. Incidentsignals 734 are reflected or scattered from the surface of the workpiece736 and the scattered signals 735 are captured by the detector 740.Alternatively, a contact system such as that illustrated in FIG. 5Emight also be used.

FIGS. 7B and 7C illustrate alternative embodiments of the TMM 712. Inthe embodiment of FIG. 7B, at least a portion of the measurement moduleor at least a portion of the inspection system associated with themeasurement module is positioned inside of an internal space of chamber713 of the TMM 712. More specifically, a measurement region 715 isdefined and is located within a dedicated area of the internal space ofthe transfer chamber 713. The signal source and signal detector elementsof an inspection system are located externally of the transfer chamberinternal space 713 while the workpiece support mechanism 738 andtransfer mechanism 714 for supporting a workpiece 736 are containedwithin the transfer for chamber 713. To that end, the inspection signals734 pass through an appropriate access port 750 that is effectivelytransparent to the passage of the inspection signal from the inspectionsystem and into the internal space to engage workpiece 736 positioned inthe measurement region 715. As noted, the inspection signal mightinclude an electromagnetic signal, an optical signal, a particle beam, acharged particle beam, or some combination of such signals. The accessport 750 may be appropriately formed to operate with a specificinspection system and the sources of the inspection signal. For example,the access port might include a window, an opening, a valve, a shutter,and iris, or some combination of different structures for forming theaccess port in order to allow incident inspection signals to engage theworkpiece 736. To that end, at least a portion of the inspection system730 might be located generally above a top surface of the transferchamber 713.

In accordance with features of the invention, the support mechanism 738or transfer mechanism (whichever is supporting the workpiece formeasurement) provides movement of the workpiece 736 for scanning theworkpiece with respect to the system. Alternatively, as disclosed, theworkpiece might be stationary while the inspection system is scanned. Inone embodiment, the substrate support mechanism provides translation androtation of the workpiece, such as under the path of inspection signals734 is indicated by the reference arrows in FIGS. 7B and 7C. In thatway, measurement/metrology data may be captured and then utilized by thecontrol system 522 is discussed herein for providing active interdictionduring substrate processing and fabrication in order to providecorrections to the fabrication process to address data indicating thatsubstrate layers and/or features or out of specification or to correctnon-conformities or defects that are detected.

In accordance with one feature of the invention, the transfer mechanism714 takes workpieces from one or more of the processing modules 720a-720 e and before moving it onto another processing chamber, passes thesubstrate through the measurement region 715 of the TMM. For example,the mechanism 714 may direct the workpiece 736 onto a support mechanism738 wherein it is translated and/or rotated with respect to the signals734 of one or more inspection systems.

FIG. 7C illustrates an alternative embodiment of the TMM of theinvention. Therein, the measurement module is positioned generallyentirely inside of the internal space of the transfer chamber 713. Thatis, the support mechanism 738 as well as the inspection system 730 andcomponents are contained inside of the transfer measurement module 712.Generally, the components of the measurement module including theinspection system as well as support mechanisms are positioned in thedefined measurement region 715 and thus have their own dedicated areawithin the internal space or chamber of the TMM.

The embodiments of the TMM illustrated in FIGS. 7B and 7C incorporatecontactless inspection systems 730 wherein inspection signals aredirected onto the workpiece. Alternatively, as noted, the inspectionsystem 730 might also include a contact measurement system such as thatshown in FIG. 5E that physically contacts the workpiece or contacts thesupport mechanism or does both in order to measure data associated withan attribute of the workpiece. Furthermore, while FIGS. 7B and 7Cillustrate placement of a workpiece 736 onto a support mechanism 738,the transfer mechanism or robot 714 might actually act as a supportmechanism for moving the workpiece with respect to the inspection systemas illustrated in FIG. 5C. Still further, the inspection systems for themeasurement modules used in a TMM might also incorporate a stationaryworkpiece wherein the inspection system itself moves as shown in FIG.5D. Similarly, the inspection system 530 might be incorporated as partof or embedded with the support mechanism as illustrated in FIG. 5F.

By incorporating at least, a portion of a measurement module to bepositioned inside of an internal space of the TMM, efficiencies can berealized because the workpiece can be passed into a measurement regionwhile being transferred between processing modules. Utilization of thetransfer mechanism 714 as a support mechanism for the workpiece isparticularly suitable for the TMM as illustrated in FIG. 7A. To thatend, FIGS. 7D and 7E illustrate another embodiment of the invention,wherein an inspection system may be incorporated directly onto atransfer mechanism 714. As illustrated, an inspection system 730 mightbe coupled to the transfer mechanism 714 to move with the workpiece. Inthat way, when the workpiece moves between processing chambers, it canbe engaged by the inspection system 730 as it is being moved forobtaining measurement data. Referring to FIG. 7E, the inspection system730 might be incorporated above and/or below the robot associated withthe transfer mechanism in order to obtain data from either surface of aworkpiece 736 carried by the transfer mechanism. The system asillustrated in FIGS. 7D and 7E might be utilized to obtain data whilethe workpiece is actually being moved to another separate inspectionsystem. As such, a transfer mechanism 714 is illustrated in FIGS. 7D and7E might be incorporated with various embodiments of the measurementmodules or transfer measurement modules as disclosed herein.

Certain of the measurement scenarios and inspection systems as describedherein are shown to be directed to what is essentially a top surface ofthe workpiece, or essentially the work surface of the workpiece on whichdevices are formed. Alternatively, measurements may be desired on thebottom surface of a workpiece. That may be done by positioning theworkpiece onto a support mechanism that incorporates embeddedmeasurement systems as shown in FIG. 5F. Alternatively, as illustratedin FIGS. 7F and 7G, inspection systems might be arranged in a TMM 712such that a bottom surface of a workpiece is measured, either fromwithin the internal space of the chamber 713 as in FIG. 7F or externallyas illustrated in FIG. 7G.

As will be appreciated, while the embodiments disclosed in FIGS. 7A-70show a single inspection system, multiple systems 730 might be utilizedinside transfer measurement module 712 to take various differentmeasurements on the workpiece and thereby provide inputs to the activeinterdiction control system 522 for taking steps to correct orameliorate any detected non-conformities or defects. The measurementsmay be taken on-the-fly within the processing environment of the TMM,which may be a controlled environment or under vacuum. In that way,various measurements of features and/or attributes may be determinedwithin a contaminant-free zone in the transfer module. Inside of thetransfer measurement module (TMM) workpieces may move from processing tothe measurement region 715 without breaking vacuum. The transfermeasurement module 712 provides a module that may be incorporated into acommon manufacturing platform with a plurality of different processingchambers as illustrated. Since the workpiece is moved between variousprocessing modules in the completion of a processing sequence, thesubstrate may be passed through the measurement region 715 without asignificant increase in time in the overall processing sequence.Thereby, measurement data is readily gathered in real-time, and may beprocessed by the control system 522 discussed herein to affect orcorrect the processing sequence, as necessary, depending upon themeasured data.

In accordance with features of the invention, the substrate supportmechanism 538, 638, 738 is utilized herein provide multiple degrees offreedom and motion in order to take necessary measurements on theworkpiece surface within the measurement module or transfer measurementmodule (TMM). For example, multi-axis X-Y-Z translation is provided aswell as rotation of the substrate. The support mechanism may providesub-micron level control of the movement of the workpiece for thepurposes of capturing data. In accordance with one embodiment of theinvention, a mechanical drive system may be utilized in the supportmechanism and platform to provide the multiple degrees of freedom inmotion. In an alternative embodiment of the invention, a magneticallylevitated and rotating support platform may be utilized. Such a supportmechanism and platform may reduce some of the possible contaminationassociated with a support platform utilizing mechanical drive systems.

Specifically, FIGS. 7H and 7I illustrate a support platform 770 thatincorporates the rotatable workpiece holder 772. The holder 772, forexample, might be made of aluminum. Beneath the rotating holder 772, aheater element 774 may provide heat to the workpiece holder 772. Theworkpiece holder 772 is coupled to a magnetic levitation rotor element776 through an appropriate adapter 778, which may also be made ofaluminum. Generally, the magnetic levitation rotor element 776 may bering-shaped. FIG. 7I illustrates only a partial cross-section of theworkpiece holder 772. FIG. 7H illustrates the entire workpiece holder772 coupled with a linear translation mechanism 780.

The support mechanism platform 770 also incorporates a magneticlevitation stator or element 790 which surrounds and is proximate to themagnetic levitation rotor element 776. Through the interaction of therotor element 776 and stator element 790, the workpiece holder 772 maybe rotated about a base 792.

For translation of the support platform 770, the base element 792 androtating workpiece holder 772 are mounted to a translation mechanism794. The translation mechanism 794 may incorporate one or moretranslation rods 780 which are appropriately coupled through mountingelements 782 to the base element 792 of the support platform. Thesupport platform 770 may be incorporated into a vacuum environment andspecifically may be incorporated into the various measurement modules ortransfer measurement modules as disclosed herein for providing rotationand translation of a workpiece in proximity to one or more inspectionsystems for capturing metrology data. The support platform 770 may betranslated at a rate of up to 300 mm/s at the direction of a controlsystem in order to provide desirable measurement data. The workpieceholder may be rotated at a rate of up to 120 RPM, for example, as it istranslated. Heating may also be provided through the heat element 774.The translation rods 780 may be also coupled to additional translationmechanisms for moving the workpiece holder 772 along another axis aswell as an elevation mechanism (not shown) for elevating the supportplatform 770. While the workpiece holder 772 is located within themeasurement module or a transfer measurement module as disclosed herein,various elements of the translation mechanism, such as portions oftranslation rods 780 and other mechanisms, including the drive motorsfor such mechanisms, may be located outside of the measurement module orthe transfer measurement module. One or more protection layers ofvarious materials may be applied to the rotation components to preventoutgassing and potential contaminants from entering the chamber andlanding on the substrate. Details of a suitable support platform 770 arefurther described in U.S. Patent Application Publication Serial No.US2018/0130694 entitled “Magnetically Levitated and Rotated Chuck forProcessing Microelectronic Substrates in a Process Chamber” filed Nov.8, 2017 and incorporated by reference herein in its entirety.

FIGS. 8, 8A, and 8B illustrate alternative embodiments of the inventionwherein defined measurement regions are implemented not only within thetransfer measurement module, but also within a pass-thru chamberutilized by a transfer measurement module to move workpieces between thetransfer measurement module and one or more processing modules or othertransfer modules. Such measurement regions might be located within adedicated area of the internal space of a pass-thru chamber and areaccessible by the transfer mechanism moving workpieces for the purposesof positioning a workpiece within the measurement region. This may bedone before or after the workpiece has been processed in a processingmodule. In accordance with features of the invention, an inspectionsystem is associated with one or more measurement regions and theinspection system is configured to engage a workpiece that is positionedin the measurement region for measuring data associated with anattribute of the workpiece. Referring to FIG. 8A, a transfer measurementmodule 812 a is coupled with a transfer module 812 b through apass-through chamber 830. The transfer measurement module 812 a willinclude one or more dedicated measurement regions 815 therein associatedwith appropriate inspection systems for gathering measurement data.Transfer module 812 b is shown as a typical transfer module withoutmeasurement capability, although that transfer module might alsoincorporate one or more dedicated measurement regions and inspectionsystems. Each of the modules 812 a, 812 b act as a platform forsupporting one or more processing modules 820 a-820 e. The associatedtransfer mechanisms 814 will move workpieces through a processingsequence and into and out of various modules of the processing modulesunder the control of an active interdiction control system 522 asillustrated. In that way for example, the workpiece might be movedthrough a processing sequence associated with the platform defined bytransfer measurement module 812 a and then moved to a differentprocessing sequence passing the workpiece through the pass-thru chamberto engage the other transfer mechanism 814 within transfer module 812 b.

In accordance with one embodiment of the invention, the pass-thruchamber has an internal space 832 to allow for movement of the workpiecebetween the transfer measurement module 812 a and another transfermodule 812 b, or as illustrated in FIG. 8B, a processing module. Each ofthe transfer modules may incorporate a transfer chamber 813 that has aninternal space that houses a transfer mechanism 814. As noted thetransfer mechanism is configured to move various workpieces through theinternal space and selectively into and out of the various processingmodules or the pass-thru chamber 832. A dedicated measurement region 815is positioned within the pass-thru chamber internal space 832. Themeasurement region 815 within the pass-thru chamber is accessible byeither of the transfer mechanisms 814 for positioning a workpiece inthat measurement region before or after the workpiece has been processedin one of the adjacent processing modules. The measurement region oftransfer chamber 830 will include one or more inspection systems asdescribed herein that are configured to engage a workpiece that ispositioned in the measurement region and is operable for measuring dataassociated with an attribute on the workpiece. In that way, measurementor metrology data may be gathered as the workpiece is moved betweenadjacent processing platforms or into and out of other processingmodules.

For example, FIG. 8B illustrates an alternative arrangement utilizing apass-thru chamber 830. The platform 800 may include a transfermeasurement module 812 a, for example, that incorporates a number ofprocessing modules as illustrated. The pass-thru chamber 830 may passthrough to another processing module 820 f rather than to anothertransfer module or transfer measurement module as depicted in FIG. 8A.Thus, in accordance with embodiments of the invention, measurementmodules and/or inspection systems are incorporated onto a commonplatform with various processing modules by incorporating measurementregions and inspection systems within other areas, including a pass-thruchamber that is utilized for moving substrates between platforms orbetween processing modules.

FIGS. 9, 9A, and 9B illustrate still another embodiment of the inventionwherein one or more inspection systems are coupled with a transfermodule specifically a transfer chamber of the module. Turning to FIG. 9,a platform 900 is illustrated that incorporates the transfer module 912and a plurality of processing modules 920 a-920 e. The transfer moduleincludes transfer chamber 913 that defines an internal space for themovement of workpieces. As illustrated, the transfer chamber 913 alsoutilizes one or more transfer ports 919 that are disposed around aperimeter of the transfer chamber and may be accessed through gatevalves G. As shown in FIG. 9, the transfer ports 919 coincide with entryto one or more processing modules and thus the transfer ports areopposite corresponding processing modules. Transfer mechanism 914 ispositioned inside an internal space of the transfer chamber 913 and isconfigured to move a workpiece generally along a horizontal plane 917within the chamber internal space. The transfer mechanism 914selectively moves workpieces into and out of one or more processingmodules that are positioned opposite corresponding transfer ports inmodule 912.

One or more inspection systems 930 are coupled with the transfer chamber913 and will engage in measurement regions 915 that coincide withtransfer ports 919. The inspection systems will include components asdiscussed herein and may include a sensor access port or aperture 950 asillustrated in FIG. 9A that is disposed opposite the horizontal plane917. Each of the inspection systems and specifically the sensorapertures are located within the perimeter of transfer chamber 913 andprovide access to workpieces as they move into and out of the processingmodules through corresponding transfer ports 919 as illustrated in FIGS.9A-9B. FIG. 9A illustrates an inspection system 930 that directsinspection signals 934 from a signal source 932 through aperture 950 andthen into the transfer chamber to engage a workpiece moving horizontallyfrom the transfer chamber 913 through transfer ports 919 and into aprocessing module. Appropriate detectors 940 then detect or measurescattered signals 935 for obtaining measurement data.

In one embodiment of the invention, the inspection system might be anoptical detection system that utilizes a light source 932 and an imagecapture device 940. Then the data associated with the image capture maybe processed, such as by the active interdiction control system 522. Aninspection system including an image processing system, as implementedthrough the active interdiction control system, may analyze surfacecomponents of the captured image. Alternatively, such an opticaldetection system may utilize pattern analysis, or thickness analysis orstress analysis associated with images captured by the optical detectionsystem. Such measurement data may then be utilized in accordance withthe invention for providing active interdiction and correctiveprocessing associated with the detection of any non-conformities ordefects.

FIG. 9B illustrates an alternative embodiment of the invention, whereininspection system 930 might be located entirely within chamber 913 ofthe transfer module 912 and positioned in respective areas 915 proximateto the transfer ports to the processing modules as illustrated tointernally be disposed opposite the horizontal plane 917 in which theworkpiece moves. Inspection system 930 captures images associated withthe surface of the workpiece that may then be processed through theactive interdiction control system for providing surface analysis,pattern analysis, thickness analysis, stress analysis, etc. In that way,measurement data may be obtained, on-the-fly, as workpieces are movedinto and out of various processing modules in the common platform 900.

FIGS. 10A and 10B illustrate other alternative platforms, 1000 and 1000a, incorporating features of the invention, wherein substrates areprocessed through a plurality of different processing modules, that mayinclude one or more etch modules and one or more film-forming modules incombination with one or more measurement/metrology modules to providemeasurement data utilized by an active interdiction control system forcontrolling the overall process sequence in correcting non-conformitiesand defects. Platform 1000 may incorporate a distributed transfer systemthat incorporates one or more transfer mechanisms 1014 for selectivelymoving workpieces through the various modules of the platform. Referringto FIG. 10A, the distributed system incorporates at least one vacuumchamber 1002 that is accessed through front end modules 1001. The vacuumchamber 1002 may be a unitary chamber that defines generally a singlechamber that has a plurality of ports 1004 for coupling with the chamber1002 that contains the distributed transfer system. Alternatively, alsoas illustrated in FIG. 10A, the vacuum chamber 1002 might be separatedinto a plurality of internal vacuum chambers 1010 that are coupledtogether through a plurality of respective pass-thru ports 1012 asillustrated. In such an embodiment, the transfer mechanism utilized mayincorporate a plurality of transfer mechanisms 1014 as illustrated thatare associated with internal vacuum chambers.

Various processing modules maintained on platform 1000 might include oneor more film-forming modules, such as selective deposition (SD) modules1030. Furthermore, the platform may include one or more etch modules1032 and one or more clean modules 1034. Also, a plurality ofmetrology/measurement modules 1036 may be incorporated. One or moreother processing modules 1038 may also be incorporated on platform 1000,and thus the type of processing and measurement/metrology modulesincorporated on the common manufacturing platform is not limited to whatis illustrated in FIG. 10A. Platform 1000, including the variousprocessing modules as well as measurement/metrology modules are coupledwith an active interdiction control system 1040 to provide measurementdata, in-situ processing data, and other data that control a processingsequence in accordance with the invention. That is, measurement datathat indicates non-conformities and/or defects is utilized by the activeinterdiction control system for corrective processing and to controlvarious of the process modules and movement of the workpiece through theplatform.

The active interdiction control system 1040 also controls the pressurewithin the vacuum chamber 1002 and also within the individual internalvacuum chambers 1010 through which the substrate is transferred. Forexample, control system 1040 will control pressure differentials betweenvarious of the internal vacuum chambers 1010 when the workpiece istransferred within the distributed transfer system as illustrated in theplatform 1000. Furthermore, the control system 1040 will control andmaintain a treatment pressure differential between the distributedtransfer system vacuum chamber 1002 and vacuum chambers associated withone or more of the various processing modules. In accordance withanother feature of the invention, the platform 1000 which incorporatesthe vacuum chamber 1002 and one or more transfer mechanisms 1014 mightalso incorporate one or more inspection systems 1050 for obtainingmeasurement data produced by the control system 1040, as workpiecesproceed through the platform 1000. As illustrated, with an internalchamber 1010 including a transfer mechanism 1014 and separate inspectionsystem, each of the chambers 1010 may act as a transfer measurementmodule (TMM) as discussed herein. One or more of the pass-through ports1012 might include a load lock mechanism to form a staging area in oneof the vacuum chambers 1010 to store one or more workpieces.

In addition to various processing modules as illustrated, the platform1000 may incorporate one or more batch process modules 1060 that providebatch processing, such as for atomic layer deposition, for example.Associated with the batch processing modules 1060 are batch/debatchstage 1070 and then eject/redesign stage 1072, wherein various of theworkpieces going into or out of batch processing might be staged. Suchchambers or areas may also be utilized as storage chambers while thecontrol system 1040 is providing the desired pressure differentialsbetween the internal vacuum chamber 1002 and one or more of the chambersassociated with the processing modules.

In accordance with one aspect of the invention, as workpieces movethrough platform 1000, and into and out of various of the processingmodules and internal vacuum chambers 1010, environmental conditions aremaintained between internal vacuum chamber 1002 and a chamber of theprocessing module when the workpieces are transferred therebetween. Theenvironmental conditions may comprise at least one of pressure, gascomposition, temperature, chemical concentration, humidity, or phase.The control system 1040 will maintain that environmental condition(s) asnecessary for processing and transfer. Also, system environmentalconditions might be maintained in the vacuum chamber 1002 between thevarious internal sections or internal vacuum chambers 1010 by thecontrol system 1040. Again, such environmental conditions may include atleast one of pressure, gas composition, temperature, chemicalconcentration, phase, humidity, etc. Environmental conditions that aremaintained between the various sections or internal chambers 1010 andone or more other internal vacuum chambers 1010 may be based at least inpart on the type of measurement or scan that may be performed by theinspection systems 1050 on a substrate that is disposed within aparticular internal vacuum chamber 1010. Such environmental conditionsmay include pressure, gas, composition temperature or phaseconcentration. As noted, for processing, it may be necessary to maintaina system pressure differential between the various internal vacuumchambers when the substrate is transferred within platform 1000 and thecontrol system 1040 maintains such a condition. Furthermore, it might benecessary to maintain a treatment pressure differential between thevacuum chamber 1002 and one or more of the chambers of a processingmodule when the substrate is transferred between vacuum chamber 1002 anda process module. To that end, the batch stage 1070 and eject stage 1072as staging areas for various workpieces within vacuum chamber 1002 untilthe system pressure differential or the treatment pressure differentialis achieved. Still further, it may be desirable to maintain systemenvironmental conditions based on the type of measurement or metrologyprocess being performed. Such environmental conditions may includepressure, gas composition, temperature or phase concentration.

Platforms 1000, 1000 a can host a variety of processing modulesincluding, but not limited to, film-forming equipment, etchingequipment, deposition equipment, epitaxial equipment, cleaningequipment, lithography equipment, photo-lithography equipment,electron-beam lithography equipment, photo-sensitive orelectron-sensitive material coating equipment, electromagnetic (EM)treating equipment, ultraviolet (UV) treating equipment, infrared (IR)treating equipment, laser beam treating equipment, thermal treatingequipment, annealing equipment, oxidation equipment, diffusionequipment, magnetic annealing equipment, ion implant equipment, plasmaimmersion ion implant equipment, cryogenic or non-cryogenic aerosol ornon-aerosol dry cleaning equipment, neutral beam equipment, chargedparticle beam equipment, electron beam treating equipment, ion beamtreating equipment, gas cluster beam equipment, gas cluster ion beamequipment, etc. The processing modules can include dry-phase equipment,liquid-phase equipment, vapor-phase equipment, etc. Additionally, theprocessing modules can include single substrate processing equipment,mini-batch processing equipment (e.g., less than 10 substrates), batchprocessing equipment (e.g., greater than 10 substrates), etc.

FIGS. 10C-10E illustrate exemplary processing modules that may beimplemented with the common platform embodiments as discussed herein.FIG. 100 illustrates a film-forming or deposition module 1070 that willgenerally include a chamber 1072. The film-forming module 1070 mightinclude a vacuum deposition chamber, or an atmospheric coating chamber.Module 1070 might also include a liquid dispensing system 1074 such asfor an atmosphere coating chamber or an RF power source 1076 such as forpowering a plasma in a deposition chamber 1072. The module 1070 mightalso incorporate a liquid source bubbler 1078 that can be coupled to aliquid dispensing system 1074 for providing the proper material phaseinto the chamber 1072 such as a deposition chamber. Film forming module1070 might also utilize one or more sputter targets 1080 and might becoupled to one or more gas sources 1081 a, 1081 a for the purposes offilm deposition in a deposition chamber 1072.

FIG. 10D illustrates a film removal or etch module 1082 thatincorporates a processing or etch chamber 1083. For example, etchingmodule may include a plasma etching module, a plasma-free etchingmodule, remote plasma etching module, gas-phase etching module atatmospheric or sub-atmospheric conditions (e.g., vacuum), vapor-phaseetching module, liquid-phase etching module, isotropic etching module,anisotropic etching module, etc. Module 1082 may, for instance, includea liquid-phase, vapor-phase, or gas-phase dispensing or distributingsystem (e.g., 1085 a, 1085 b, 1086), pressure control elements,temperature control elements, substrate-holding and controlling elements(e.g., electrostatic clamping chuck (ESC), zoned temperature controlelements, backside gas system, etc.), and a power source 1084 (e.g., RFpower source) for generating plasma in the etch chamber 1083.

FIG. 10E illustrates a clean module 1088 having a cleaning chamber 1089for appropriately receiving substrates. For example, clean module 1088may include a wet clean module, a dry clean module, a spin-type cleanmodule, a bath-type clean module, a spray-type dispense clean module, aneutral beam clean module, an ion beam clean module, a gas cluster beamclean module, a gas cluster ion beam clean module, a cryogenic ornon-cryogenic aerosol clean module, etc. The clean module 1088 mayinclude a liquid source, a bath, a liquid dispense or spray nozzle 1090,a spin chuck, nested liquid dispense capture baffles, pressure controlelements, temperature control elements, etc. The clean module 1088 mayalso incorporate a gas source, a cryogenic cooling system 1092, a gasnozzle, an aerosol nozzle, pressure control elements, temperaturecontrol elements, etc.

As noted, the platform 1000 might be utilize to stage one or moresubstrates for storage, such as while a corrective processing procedureis under way or process parameters in the platform are adjusted. To thatend, the batch/debatch chamber 1070 or the eject chamber 1072 mayincorporate a load lock at one of the adjacent pass-thru ports 1012 suchthat one or more of the individual internal vacuum chambers 1010 canoperate as a separate staging area within the larger overall platform sothat various workpieces may be stored within at least one internalvacuum chamber. Furthermore, the batch stage 1070 and eject stage 1072might also act as a staging area to stage substrates for the batchprocessing module 1060 or while system parameters are adjusted.

FIG. 10B illustrates another possible platform layout similar to theplatform of FIG. 10A with similar reference numerals utilized forvarious of the processing modules, control systems, and components ofFIG. 10B. Turning to FIG. 10B, platform 1000 a may include one or morefilm-forming modules 1030 and etch modules 1032 that are coupled withTMM modules 1010 for moving workpieces through the platform. Also,measurement modules 1036 may be incorporated onto the platform fordetecting non-conformities and defects in accordance with the invention.Platform 1008 might also include cleaning modules such as a wet cleanmodule 1034 a or a dry clean module 1034 b. Furthermore, platform 1000 amight incorporate one or more measurement modules 1036 that areimplemented for batch measurement. As illustrated, opposite batchprocess module 1060, one or more measurement modules 1036 may beimplemented so that measurements may be taken, and measurement/metrologydata gathered while workpieces are in a batch and before they areejected and/or realigned through an eject stage 1072. Platform 1000 a isin the control of an active interdiction control system 1040 asillustrated and workpieces can be moved back and forth in a generallylinear fashion between the various processing modules and measurementmodules in accordance with the invention to detect non-conformities anddefects and also to provide corrective processing to the workpieces.

Active Interdiction and Workpiece Processing Examples

As described herein the active interdiction control system is configuredfor performing corrective processing based in part on measured data fromthe workpiece. Other data, such as process parameter data reflective ofthe processing parameters or settings of one or more processing modulesmay also be input to the active interdiction control system, as well asplatform performance data for the common manufacturing platform. Thedata is processed by the active interdiction control system fordetermining non-conformities and defects in the workpiece and fordetermining a path of corrective processing to be performed in theplatform during an active interdiction. As noted, the correctiveprocessing may be performed in processing modules upstream or downstreamin a process sequence when non-conformities are detected. The activeinterdiction control system is coupled with the various measurementmodules and TMMs of the platform and processes the measured data andother data for controlling movement and processing of the workpiece inthe process sequence.

In accordance with one feature of the invention, the correctiveprocessing may include performing a remedial process sequence in theoverall process sequence. For example, the remedial process may includecleaning a workpiece and/or removing a film or a portion of a film.Alternatively, an adjustment process sequence might be performed. Stillfurther, the corrective processing might the simple ejection of theworkpiece from the platform and process sequence if it cannot becorrected. In either case, an operator might be informed of a detectednon-conformity.

FIG. 11 illustrates an active interdiction control system 1110 andcomponents 1120 for realizing the invention. The active interdictioncontrol system might be located entirely or at least partially with themanufacturing platform and will generally be executed using a computerdevice having at least one processor. The components 1120 forimplementing the active interdiction control system 1110 may be part ofthe computer used for executing the active interdiction control systemor may be resources that are called upon by the active interdictioncontrol system, such as over a network. Therefore, the various hardwarelayouts set forth herein are not limiting.

FIG. 12 illustrates an exemplary hardware and software environment foran apparatus 1210 suitable for providing the active interdiction controlsystem of the invention. For the purposes of the invention, apparatus1210 may represent practically any computer, computer system, orprogrammable device e.g., multi-user or single-user computers, desktopcomputers, portable computers and devices, handheld devices, networkdevices, etc. Apparatus 1210 will hereinafter be referred to as a“computer” although it should be appreciated that the term “apparatus”may also include other suitable programmable electronic devices.

Computer 1210 typically includes at least one processor 1212 coupled toa memory 1214. Processor 1212 may represent one or more processors (e.g.microprocessors), and memory 1214 may represent the random access memory(RAM) devices comprising the main storage of computer 10, as well as anysupplemental levels of memory, e.g., cache memories, non-volatile orbackup memories (e.g. programmable or flash memories), read-onlymemories, etc. In addition, memory 1214 may be considered to includememory storage physically located elsewhere in computer 1210, e.g., anycache memory in a processor 1212, as well as any storage capacity usedas a virtual memory, e.g., as stored on a mass storage device likedatabase 1216 or any external database or other computer or systemillustrated generally as resource 1230 coupled to computer 1210 directlyor via a network 1232.

Computer 1210 also typically receives a number of inputs and outputs forcommunicating information externally. For interface with a user oroperator, computer 1210 typically includes one or more user inputdevices coupled through a human machine interface (HMI) 1224. Computer1210 may also include a display as part of the HMI for providing visualoutput to an operator in accordance with the system of the inventionwhen non-conformities are detected. The interface to computer 1210 mayalso be through an external terminal connected directly or remotely tocomputer 10, or through another computer communicating with computer1210 via a network 18, modem, or other type of communications device.

Computer 1210 operates under the control of an operating system 1218 andexecutes or otherwise relies upon various computer softwareapplications, components, programs, objects, modules, data structures,etc. indicated generally as application 1220. The various components1120 as shown in FIG. 11 may be part of the applications on the computer1210 or might be accessed as a remote resource 1230 as shown for morerobust processing. Part of the application and processing will alsoinclude various data structures 1222 and data as noted herein that mayinclude for example the measurement data, process parameter data andplatform performance data (e.g. database application 26). Computer 1210communicates on the network 1232 through an appropriate networkinterface 1226. The computer for implementing an active interdictionsystem as disclosed will connect directly or through a network with themanufacturing platform 1240 and one or more of its control elements forthe purposes of gathering data from the manufacturing platform andcontrolling the process sequence for active interdiction.

In general, the routines executed to implement the embodiments of theinvention, whether implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions will be referred to herein as “computer program code”, orsimply “program code”. The computer program code typically comprises oneor more instructions that are resident at various times in variousmemory and storage devices in a computer, and that, when read andexecuted by one or more processors in a computer, causes that computerto perform the steps necessary to execute steps or elements embodyingthe various aspects of the invention. Moreover, those skilled in the artwill appreciate that the various processing components and tools of theactive interdiction control system are capable of being distributed as aprogram/application in a variety of forms and locations.

It should be appreciated that any particular program nomenclature thatfollows is merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature. Furthermore, given the typically endlessnumber of manners in which computer programs/applications may beorganized into routines, procedures, methods, modules, objects, and thelike, as well as the various manners in which program functionality maybe allocated among various software layers that are resident within atypical computer (e.g., operating systems, libraries, APIs,applications, applets, etc.), or in external resources it should beappreciated that the invention is not limited to the specificorganization and allocation of program functionality described orillustrated herein. Those skilled in the art will recognize that theexemplary environment illustrated in FIG. 12 is not intended to limitthe present invention. Indeed, those skilled in the art will recognizethat other alternative hardware and/or software environments may be usedwithout departing from the scope of the invention.

Referring to FIG. 11, the active interdiction control system may providepattern recognition for predicting the existence of a non-conformity. Tothat end, the active interdiction control system includes a patternrecognition component, such as pattern recognition engine 1122 that isoperable to extract and classify data patterns from the measured andpredict whether or not a non-conformity exists based on the measureddata. For example, certain features of a workpiece may be indicative ofnon-conformities and irregularities in data and may be reflected inpatterns found in the measured data. Pattern recognition can compensatefor measurement sophistication, or lack thereof, with data volume, oradditional data. Measurement of multiple variables can be combinedand/or correlated to identify non-conformities or irregularities in thedata. In doing so, less sophisticated measurements can be made andcorrelated to achieve the same outcome of a more sophisticatedmeasurement system. As an example, an optical ‘fingerprint’ can becreated for a processed workpiece, representative of acceptableprocessing behavior. Deviations of the ‘fingerprint’ can be recognizedas pattern shifts, which in turn, can identify opportunity forcorrective action, e.g., perform corrective action in an upstream and/ordownstream process, or rework an upstream process by removing theprocess outcome and repeating, etc. The pattern recognition engine 1122may implement a deep learning architecture or engine 1124 as shown thatmight use one or more neural networks and supervised or unsupervisedlearning for implementing the pattern recognition. The deep learningengine 1124 might implement multi-variate analysis (MVA), for example,to analyze non-conformities or irregularities and determine a possiblecause for use to do corrective processing. One type of multi-variateanalysis includes Principal Components Analysis (PCA). PCA is astatistical procedure that transforms a set of observations of possiblycorrelated variables into a set of principal components. Each principalcomponent, e.g., eigenvector, is associated with a score, e.g.,eigenvalue, and the principal components can be sorted by the value ofthe score in descending order. In doing so, the first principalcomponent represents the greatest variance in the data in the directionof the corresponding principal component within the n-dimensional spaceof the transformed data set Each succeeding principal componentpossesses the highest variance under the condition it is orthogonal tothe preceding components. Each principal component identifies the‘weighting’ of each variable in the data set. Subsequent observationscan be projected onto one or more principal components, e.g., the firstprincipal component and/or other components, to compute a score (e.g.,score A from the vector product of a new observation with the firstprincipal component), or mathematical manipulation of one of more scores(e.g., score A+score B/score C, etc.). For example, light scattered froma processed workpiece, either from a single location or multiplelocations, can represent an observation. When coupled with pluralobservations, a model composed of one or more principal components canbe established, and subsequently used to ‘score’ a processed workpiece.When a score, or sequence of scores, deviates from a defined ‘normalbehavior’, or acceptable process window, corrective action can proceed,i.e., perform corrective action in an upstream and/or downstreamprocess, or rework an upstream process by removing the process outcomeand repeating, e.g.

The pattern recognition engine may correlate an extracted data patternwith a learned attribute on the workpiece. The pattern recognitionengine may implement a correlation engine 1126 that accesses one or morelearned attributes, 1128, such as in a database 1132 in order tocorrelate measured data in the form of a data pattern with a learnedattribute. For example, one learned attribute might include a defect onthe workpiece, such as one or more particle contaminants. Such a defectcould be correlated with the measured data pattern for detecting anon-conformity to be addressed. In other embodiments, the defect mightindicate an out-of-tolerance condition for a workpiece attribute. Forexample, the workpiece attribute that is out-of-tolerance might includea thickness, a critical dimension (CD), a surface roughness, a featureprofile, a pattern edge placement, a void, a loss of selectivity, ameasure of non-uniformity, or a loading effect. Such defects or variouscombinations of such defects may be used for pattern recognition ofnon-conformities by the active interdiction control system.

In another embodiment, the learned attribute, rather than being adefect, might include a probability of a defect on the workpiece. Such alearned attribute might be correlated to the measured data forpredicting the existence of a non-conformity. As noted, the activeinterdiction control system will implement one or more human interfacecomponents, such as a display component for visualization of a region ofa workpiece to show to an operator where a non-conformity exists.

The correlation engine/component 1126 might also be used for predictingwhether or not a non-conformity exists. Specifically, measured data isobtained in two or more areas of a workpiece. The correlation engine1126 uses the measured data from the multiple locations and based on acorrelation of the location measured data, the existence of anon-conformity may be predicted.

In accordance with another feature of the invention, the artificialintelligence features are used by the active interdiction controlsystem. Specifically, machine learning in the form of an autonomouslearning component or engine 1130 might be implemented by the system asdiscussed further herein below. The autonomous learning engine receivesthe measured data and generates a knowledge. That knowledgecharacterizes the measurement data 1136 and performance of the processsequence, and upon the detection of a non-conformity decides an actionplan or corrective processing plan to correct the process sequence inthe event a non-conformity exists. The autonomous learning engine willalso implement one or more of the process parameter data 1138 that maybe associated with measured or diagnostic data for a process module andplatform performance data 1140 associated with the manufacturingplatform and the process modules thereon. The process parameter data andplatform performance data are combined in the autonomous learning enginewith the measurement data for forming the knowledge. The machinelearning provided by the autonomous learning engine may incorporatesupervised learning that maps inputs like the measurement data tooutputs that might be used to determine corrective processing.

Alternatively, the autonomous learning engine might use cluster analysisor clustering to group various defects, for example, for determining ifa non-conformity exists and determining the corrective processing foraddressing the non-conformity.

Alternatively, the autonomous learning engine might use dimensionalityreduction algorithms, such as, for example, determining propercorrective processing steps from a number of different processing stepsthat might be used to address a detected non-conformity.

Alternatively, the autonomous learning engine might use structuredprediction algorithms for determining the corrective processing foraddressing particular types of detected non-conformities.

Alternatively, the autonomous learning engine might use cluster analysisor clustering to group various defects, for example, for determining ifa non-conformity exists and determining the corrective processing foraddressing the non-conformity.

Alternatively, the autonomous learning engine might use anomalydetection algorithms for determining non-conformities.

Alternatively, the autonomous learning engine might use reinforcementlearning to determine corrective processing and the result.

Various combinations of the various machine learning algorithmsimplemented through the autonomous learning engine might be used forgenerating the knowledge that characterizes the measured data and theperformance of the process sequence and determines a correctiveprocessing action to address any detected non-conformities. Theautonomous learning engine may implement data associated with theprocess sequence or recipe 1134 in order to determine proper correctiveprocessing steps. Furthermore, the active interdiction control systemmay implement existing data from one or more databases 1132 forproviding the necessary machine learning and artificial intelligenceprocessing of the measured data 1136, process parameter data 1138 andplatform performance data 1140 to detect non-conformities and determinecorrective processing steps.

The measured data may be a quantitative measurement of the workpieceattribute for evaluating for determining if there is a non-conformity ordefect. Alternatively, measured data may be a proxy for a quantitativemeasurement of a workpiece attribute. As an example, a proxy allows oneto measure a desired workpiece attribute, e.g., film thickness, using aless sophisticated technique, i.e., an approximation of the workpieceattribute, and/or measure another workpiece attribute, representative ofthe desired workpiece attribute.

In one embodiment, the active interdiction control system includes aninteraction component 1136 that works with the autonomous learningengine 1130 and receives the measured data. As disclosed herein and setforth with respect to FIGS. 17-37, the autonomous learningengine/component can interface with the interaction component forprocessing the data for active interdiction and control of amanufacturing platform. The interaction component includes an adaptorcomponent that is configured for packaging the measured data andconveying the packaged data to the autonomous learning engine. Theautonomous learning engine receives the packaged data and generates aknowledge that characterizes the packaged data and the performance ofthe process sequence. The autonomous learning engine 1130 furtherincludes a processing platform that processes the packaged data whereinthe processing platform includes a set of functional units that operateon the packaged data. The set of functional units include an adaptiveinference engine that analyzes the packaged data and infers an action toperform based at least in part on a process goal for the processsequence. The functional units also include a goal component thatevolves the process goal based at least in part on one of the data or acontext change and also a memory platform that stores the knowledge. Inthe autonomous learning engine, the memory platform includes a hierarchyof memories that includes a long term memory, a short term memory, andan episodic memory. The long term memory stores a set of concepts thatincludes at least one of an entity, a relationship, or a procedure. Aconcept in the set of concepts includes a first numeric attribute thatindicates relevance of a concept to a current state of the processsequence, and a second numeric attribute that indicates a degree ofdifficulty to use the concept. The interactive component also receivesmodule diagnostic data from one or more of the plurality of processingmodules. The interactive component packages the module diagnostic datawith the measured data when it prepares the packaged data.

The interaction component also includes an interaction manager thatfacilitates data exchange with an external actor. Training data may bepart of either the packaged data or the data that is exchanged with theexternal actor, or both sets of data might include training data. Thattraining data may include at least one of an identification of a moduleprocess or variable associated with a task, e.g., prepare a surface fordepositing a thin film, deposit a thin film of a prescribed thickness ona targeted region of a workpiece, remove portion(s) of the thin filmdeposited on non-targeted regions of the workpiece, etc., a functionalrelationship among two or more module processes or variables associatedwith the task. The training data might also include a causal graph thatincludes a set of a priori probabilities associated with a set of moduleprocesses or variables related to the task and present in the causalgraph and a set of conditional probabilities that relate one or moremodule processes or variables related to the task and present in thecausal graph. Or, training data might also include a set of parametersthat describe a behavior of the process sequence.

FIGS. 17-37 illustrate one embodiment of an autonomous learningengine/component that might be implemented by the active interdictioncontrol system 1110 of the invention as set forth further below.

In accordance with one aspect of the invention, an active interdictioncontrol system is implemented with the manufacturing platforms andelements as described herein. The active interdiction control systemcaptures data from the plurality of processing modules as well as thevarious measurement modules to process data associated with an attributeof the workpiece in order to provide corrective processing on theworkpiece if necessary. More specifically, non-conformities, defects orcontamination are detected based upon measurement data and thecorrective processing is performed in the processing sequence as part ofan active interdiction. The corrective processing might be performed inprocessing modules that are either upstream or downstream in theprocessing sequence. For example, if a defect or non-conformity isdetected, there may be a corrective adjustment made in a processingmodule that is upstream or downstream in the processing sequence fromwhere the workpiece is currently located in order to try and correct thedefect or non-conformity. Conversely, in order to prevent a detecteddefect or non-conformity from occurring in the first place, one or moreprocessing modules in the processing flow might be adjusted or affectedin a corrective manner in order to prevent the defect or non-conformityfrom occurring initially, such as in subsequent workpieces.

More specifically, the manufacturing platform includes one or moreworkpiece transfer modules that are configured and controlled for movingworkpieces in the processing sequence, such as between the variousprocessing modules and the measurement modules. The active interdictioncontrol system is configured for controlling the movement and processingof the workpieces in the processing sequence and also for processing themeasured data from a workpiece, as well as in-situ data associated withthe processing modules. The active interdiction control system uses themeasured data for controlling workpiece movement in the processingsequence.

Corrective processing in the upstream and downstream directions will beselectively controlled by the active interdiction control system.Generally, the manufacturing platform will include one or morefilm-forming modules and one or more etch modules. In one controlsequence, the corrective processing is performed in an etch module afterthe workpiece has been processed in a film-forming module and thenmeasured for detecting a non-conformity or defect. Alternatively, thecorrective processing is performed in another film-forming module afterthe workpiece has been previously processed in a film-forming module. Inanother scenario, the invention provides corrective processing upon thedetection of a non-conformity or defect and the corrective processing isperformed in a treatment module, such as a cleaning module, prior toprocessing in a film-forming module.

One particular use of the present invention is in the multi-patterningprocessing such as self-aligned multi-patterning (SAMP), that includesSADP (double patterning), SATP (triple patterning), SAQP(quad-patterning), and SAOP (octo-patterning), quadruple patterning(SAQP). Such self-aligned multi-patterning techniques have enabledconventional immersion lithography to be used to print sub-resolutionfeatures, that meet the dimensional scaling needs for advancedtechnology nodes. The methodology generally includes creating a mandrelpattern on a substrate (a double mandrel for SATP) and conformallyapplying a thin film over the mandrel pattern. Then the conformal thinfilm is partially removed, leaving behind material on the sidewalls ofthe mandrel pattern. Then the mandrels are selectively removed leavingthe thin patterns from the mandrel sidewalls. Such patterns can then beused for selective etching to translate or transfer the patterns to alayer.

To facilitate SAMP processing, the common platform as illustrated hereinis equipped with etch modules, thin film-forming modules, clean modules,and other pre- or post-treatment modules. The common platform receives aworkpiece or substrate having a mandrel pattern that has been formedthereon. During a first step in the process sequence, a thin film,referred to as a spacer film, is conformally applied to the mandrelpattern. Then, in accordance with the present invention, upon completionof this step, it is important to verify the quality of the thinconformal film. This may be done by moving the workpiece to one or moremeasurement modules or passing the workpiece through a measurementregion of a transfer measurement module. In the measurement module, datais measured associated with thin film attributes. For example, the filmconformality, the film thickness and the uniformity of the filmthickness across the substrate, the composition of the film, the filmstress, etc is measured. Typically, the spacer film is silicon oxide, orsilicon nitride. Depending on the process conditions for applying thethin film, stress can be present in the film, either tensile orcompressive, which may be a detriment to further processing. Uponcompletion of the conformal film application, the substrate is subjectedto an etch step to partially remove the conformal film on horizontalsurfaces, referred to as a spacer etch. The conformal film isanisotropically removed on the surfaces between the mandrel pattern, andon the top surfaces of the mandrel, leaving behind the conformal film onthe sidewalls of the mandrel pattern. Upon completion of this step, theworkpiece might also it is important to verify the quality of the thinconformal film remaining on the mandrel pattern, by assessing the filmthickness on the mandrel sidewalls and the uniformity of the filmthickness across the substrate, the film composition or any changes ordamage to the film as a result of the etch process, the criticaldimension (CD) of the remaining multi-color pattern, i.e., mandrel andspacer, etc. Thereafter, a clean process may be applied to removeresidue, and a treatment step may be performed to compensate for any ofthe previous steps. Upon completion of the (spacer) etch step, thesubstrate is subjected to another etch step to selectively remove themandrel, while preserving the sidewall spacers, referred to as a mandrelpull etch. Upon completion of this step, it is important to verify thequality of the spacer pattern remaining on the substrate, by assessingthe spacer thickness or CD, the spacer height, the uniformity of thespacer CD and/or height across the substrate, the spacer profile orshape (e.g., sidewall angle, or variation from 90 degrees, etc.), etc.

The process sequence proceeds within a controlled environment andincludes periodic metrology steps to assess the quality of thepitch-reducing sequence, and the resultant spacer pattern remaining onthe substrate. Defects in the multiple pattern will be extended into theunderlying films on the substrate. According to embodiments describedherein, an intelligent equipment and process management system andactive interdiction control system, located either locally or remotelyon the common platform, can control the SAMP process sequence in a highvolume manufacturing environment to deliver improved yield and cyclingtime. The controller can (i) identify process steps producing substrateresults outside target specification, (ii) extract data, e.g., workpiecemeasurement and metrology data, etc., for the out-of-spec process step,emulate the impact of the out-of-spec condition on downstream processsteps, (iii) display the data or portions of the data, (iv) optimizeprocess recipe adjustment(s) to the process recipe, including upstreamor downstream process adjustments to compensate for the defect, and (v)communicate proposed recipe adjustment(s) for adoption with the processflow to correct for the out-of-spec condition. For example, if theresultant spacer pattern formed during a SAMP process exhibits adefective profile, e.g., excessive leaning, the spacer pattern transferwill result in downstream hard mask open CD variation, and possiblefailure if left uncorrected. In this instance, the intelligentcontroller can consider all corrective options from the deposition toolrecipe database, and emulate the outcome based off all downstream unitprocess recipe combinations for the problematic substrates. Thereafter,corrective action can be executed, including passing the current processstep, failing the current process step and discarding the substrate, orremediating the process step by compensating for its deficiencies eitherupstream and/or downstream of the current process step.

In another example of the present invention, corrective processing andactive interdiction might be implemented in an etch process. During etchapplications, it is important to monitor several product parameterson-substrate to ensure the integrity of the pattern transfer process.Product parameters for measurement data capture in accordance with theinvention may include feature CD (top-to-bottom), feature depth, CD anddepth uniformity (across-substrate, for dense and isolated features,etc.), etch rate and selectivity relative to materials exposed on thesubstrate, and pattern profile, including sidewall bowing, sidewallangle, corner chamfer, etc. In accordance with the invention severalcontrol parameters exist on the etch module to adjust or control theproduct parameters, and such process parameters may be captured by theactive interdiction control system as well for determining ifnon-conformities or defects have occurred in the process of a workpiece.Corrective processing might involve controlling or modifying one or moreof the process parameters for future processing of a workpiece of foraffecting a subsequent remedial process when such non-conformities anddefects are detected. Such process parameters may include chemicalcomposition of the gas-phase environment, flow rates of process gasesentering the module, pressure, source and/or bias radio frequency (RF)power for plasma generation and maintenance, substrate temperature,substrate back-side gas pressure, chamber temperature(s), direct current(DC) voltage, parameters associated with the temporal and spatialmodulation of gas flows and/or power (e.g., pulse amplitude, pulsewidth, pulse period, pulse duty cycle, etc.), etc. Some controlparameters, such as substrate temperature, and to a lesser extent powerand gas flows, can be spatially zoned to address or control processuniformity. Additionally, several process parameters exist on the etchmodule to monitor during processing that are predictive of productresults, including plasma optical emission (e.g., optical emissionspectroscopy, OES), RF power (forward and reflected) and impedance matchnetwork settings, electrical properties including voltage and current tomonitor plasma condition, stability, arcing, etc., and a host of othersensors and methodologies to monitor ion temperature (T_(i)), electrontemperature (T_(e)), ion energy distribution function (iedf), ionangular distribution function (iadf), electron energy distributionfunction (eedf), ion and/or radical flux, etc. Such process data may becaptured and used by the active interdiction control system forproviding corrective processing.

Film formation also provides a juncture in the process sequence whereinmeasurement/metrology data is a captured and if non-conformities ordefects are detected, corrective processing can be performed. Duringthin film forming applications, several product parameters on-substratemay be measured or monitored using the measurement modules and TMM ofthe invention to ensure the quality of the film formed on the substrate.For example, measurement data might be captured that is associated withfilm thickness, film conformality to substrate topography, filmcomposition, film stress, film selectivity, film planarizabilityacross-substrate, for dense and isolated features, film electricalproperties (e.g., dielectric constant), film optical properties (e.g.,refractive index, spectral absorptivity, spectral reflectivity, etc.),film mechanical properties (e.g., elastic modulus, hardness, etc.), andthe uniformity film properties, etc. Based upon non-conformitiesdetected in the workpiece, corrective processing might be implemented onan active workpiece or future workpieces in the process sequence bycontrolling several control parameters in the film-formation module toadjust or control the product parameters, including chemical compositionand phase of the film precursor, temperature of the vaporizer orampoule, carrier gas flow rate, the precursor delivery line temperature,chemical composition of the gas-phase environment in the chamber, flowrates of process gases entering the module, pressure, source and/or biasradio frequency (RF) power for plasma generation and maintenance inplasma-assisted deposition apparatus, substrate temperature, substrateback-side gas pressure, chamber temperature(s), parameters associatedwith the temporal and spatial modulation of gas flows and/or power, etc.

Additional measurement data that might be captured is directed toparticle contamination that is a source of variation during devicefabrication and can be classified as a defect. In some embodiments, thecommon platform is equipped with etch modules, film formation modules,clean modules, and other pre- or post-treatment modules, or subsetsthereof, and the platform may use process modules that include withparticle removing equipment. Thus, upon detection of particlecontamination, the active interdiction control system may implement aremedial process step using particle removal equipment that can includegas-phase or partially liquefied gas-phase beams or jets. The particleremoval beams or jets of such a process module can be cryogenic ornon-cryogenic, and may or may not include aerosols, gas clusters, etc.The common platform can also be combined with a defect inspectionmeasurement module to perform monitoring workpiece surface scans, countparticles, and identify film defects. The defect inspection module caninclude optical inspection, using dark field and/or bright fieldillumination to detect the presence of particles. Alternatively, oradditionally, the defect inspection module can include electron beaminspection. Once a defect is detected, the active interdiction controlsystem affects the process sequence in the manufacturing platform tocorrectively process the workpiece so as to remove any contaminatingparticles.

In accordance with another aspect of the invention, the data processedby the present invention by the active interdiction control system willinclude fabrication measurement/metrology data that is determined frommeasurement modules or TMMs that are implemented in a commonmanufacturing platform. Such fabrication measurement data is ameasurement of an attribute of the workpiece based partially orcompletely on the process sequence performed on the common manufacturingplatform. Such information may be combined with other data that isgathered, including process parameter data, associated with certainprocess parameters or settings of one or more of the process modules inthe common platform, as well as platform performance data that isreflective of certain parameters and settings and information about thecommon manufacturing platform.

The process parameter data may include an indication of one or moreprocess conditions executed in the processing modules. For example, theprocess conditions may be based on at least one of plasma density,plasma uniformity, plasma temperature, etch rate, etch uniformity,deposition rate, and/or deposition uniformity. Such measured processconditions might also include one of amplitude, frequency, and/ormodulation of energy that is applied to a plasma source disposed withinthe processing module. Still further, the process conditions mightinclude gas flow rates that are being flowed into the processing moduleduring the process sequence, the temperature of a workpiece holder thatis disposed within the processing module, and/or the pressure in theprocess module during the process sequence.

The platform performance data may include an indication of a platformattribute contributing to the execution of the process sequence or anindication of how long a process module has been exposed to the processsequence. Exemplary platform attributes contributing to a processsequence may include process cooling water temperature, process coolingwater flow rate, process module processing time, and/or process modulecumulative thickness.

When non-conformities are detected using the various data, includingfabrication measurement data, the process parameter data and/or theplatform performance data, the active interdiction can be performed. Theactive interdiction is performed to the process sequence either on theworkpiece measured or on a workpiece that is subsequently processed.That is, the data might be used to correct the current workpieces ormight be used later to correct subsequent workpieces that are processedso that further non-conformities do not occur.

In an alternative embodiment, measurement data might be captured in-situin a process module and used for detecting non-conformities of aworkpiece. For example, various sensors might be located inside of thechamber of a process module, such as an etch or film-formation ordeposition chamber, or an inspection system might access the internalspace of a process chamber. In such a case, the in-situ processmeasurement data might be used alone or in combination with the othermeasurement data that might be considered fabrication measurement data,and non-conformities of the workpiece may be detected based on at leastone of the gathered fabrication measurement data or the in-situ processmeasurement data. Then active interdiction might be performed in theprocess sequence to execute corrective processing of the workpiece inthe process sequence on the common manufacturing platform after themeasurement data has been gathered.

In accordance with one aspect of the invention, the correctiveprocessing of the active interdiction on a current workpiece may includea number of different paths depending on the detected non-conformity ordefect. In one exemplary path, a process might be varied within one ormore of the process modules. This might occur in a process or modulethat is upstream in the process sequence of where the workpiececurrently resides or may occur in a process or module that is downstreamin the process sequence.

The process variation to the process sequence might include exposing theworkpiece to a remedial process sequence to correct the non-conformity.The remedial process sequence might include steps taken to address orremove the non-conformity. For example, a cleaning of the workpiecemight be added as a step in the process sequence. The cleaning of theworkpiece might be handled using a cryogenically cooled spray, such aswith a chamber as shown in FIG. 10E. Furthermore, a film might beremoved from the workpiece or a portion of a film might be removed. Sucha remedial step might be performed on the common manufacturing platform.Or the remedial process sequence might be performed external to thecommon manufacturing platform.

Alternatively, the process variation may include exposing the workpieceto an adjustment process sequence to modify the detected non-conformity.The adjustment process sequence might include controlling one or moreprocess parameters or conditions of a process module based, partially orcompletely, on a real-time measurement of the fabrication measurementdata or in-situ process measurement data from which a non-conformity isdetected. The adjustment process sequence may include controlling one ormore process conditions of a processing module based, at least in part,on a model corresponding to correction of the non-conformity. The modelcan allow a user to predict the outcome of a process step in a processmodule provided a change to the incoming process recipe. Also, theadjustment process may include alternating processes between afilm-forming process, an etching process, or a film-treatment process inorder to modify the detected non-conformity.

Also, if the non-conformity is one that may not be remediated, correctedor modified, the workpiece might be discarded in the activeinterdiction.

In still another alternative, the active interdiction might includenotifying an operator of the of the non-conformity to allow the operatorto determine a path to be taken.

In accordance with another feature of the invention, in-situ processmeasurement data might be gathered in-situ in a processing module duringa process step in the sequence. The active interdiction may indicate acorrective processing step that will also occur in-situ in the sameprocessing module where the in-situ process measurement data wasobtained or gathered. That is, the workpiece might remain in the moduleand for the further processing in the same process step as previouslydone before the in-situ measurement was made.

After performing the active interdiction, the workpiece might be movedor manipulated for obtaining additional fabrication measurement data ofthe workpiece to determine impact on the non-conformity based on theactive interdiction and corrective processing. If the correctiveprocessing is successful or moving in the right direction to address thenon-conformity or defect, the process sequence might continue for theworkpiece based on the determined impact on the non-conformity.

EXAMPLES

FIGS. 13A-13E set forth one example of active interdiction in areaselective deposition for removal of undesired nuclei on a self-alignedmono layer through active interdiction.

Referring now to FIGS. 13A-13E, according to one exemplary embodiment,the manufacturing platform with an active interdiction control systemmay be configured to perform and monitor a method of area selectivedeposition on a substrate and to gather measurement data and other data.In this embodiment, the substrate 1300 contains a base layer 1302, anexposed surface of a first material layer 1304 and an exposed surface ofa second material layer 1306. In one example, the substrate includes adielectric layer 1304 and a metal layer 1306. For example, the metallayer 1306 can contain Cu, Al, T a, Ti, W, Ru, Co, Ni, or Mo. Thedielectric layer 1304 can, for example, contain SiO₂, a low-k dielectricmaterial, or a high-k dielectric material. Low-k dielectric materialshave a nominal dielectric constant less than the dielectric constant ofSiO₂, which is approximately 4 (e.g., the dielectric constant forthermally grown silicon dioxide can range from 3.8 to 3.9). High-kmaterials have a nominal dielectric constant greater than the dielectricconstant of SiO₂.

Low-k dielectric materials may have a dielectric constant of less than3.7, or a dielectric constant ranging from 1.6 to 3.7. Low-k dielectricmaterials can include fluorinated silicon glass (FSG), carbon dopedoxide, a polymer, a SiCOH-containing low-k material, a non-porous low-kmaterial, a porous low-k material, a spin-on dielectric (SOD) low-kmaterial, or any other suitable dielectric, material. The low-kdielectric material can include BLACK DIAMOND@ (BD) or BLACK DIAMOND@ II(BDII) SiCOH material, commercially available from Applied Materials,Inc., or Coral@ CVD films commercially available from Novellus Systems,Inc. Other commercially available carbon-containing materials includeSILK@ (e.g., SiLK-I, SiLK-J, SiLK-H, SiLK-D, and porous SiLKsemiconductor dielectric resins) and CYCLOTENE@ (benzocyclobutene)available from Dow Chemical, and GX-3™ and GX-3P™ semiconductordielectric resins available from Honeywell.

Low-k dielectric materials include porous inorganic-organic hybrid filmscomprised of a single-phase, such as a silicon oxide-based matrix havingCH3 bonds that hinder full densification of the film during a curing ordeposition process to create small voids (or pores). Stillalternatively, these dielectric layers may include porousinorganic-organic hybrid films comprised of at least two phases, such asa carbon-doped silicon oxide-based matrix having pores of organicmaterial (e.g., porogen) that is decomposed and evaporated during acuring process.

In addition, low-k materials include a silicate-based material, such ashydrogen silsesquioxane (HSQ) or methyl silsesquioxane (MSQ), depositedusing SOD techniques. Examples of such films include FOx® HSQcommercially available from Dow Corning, XLK porous HSQ commerciallyavailable from Dow Corning, and JSR LKD-5109 commercially available fromJSR Microelectronics.

FIG. 14 illustrates a flowchart of an exemplary process sequence on themanufacturing platform implementing the invention. The process sequence1400 includes, in step 1402, of the process flow providing the workpieceinto a measurement module of the platform or into a TMM where theworkpiece is measured and characterized in order to generate measurementdata. (Block 1404)

Referring to FIG. 15, once a workpiece has been moved to a measurementmodule or TMM that contains an inspection system, or data has beengathered in situ, in accordance with the process flow 1500 asillustrated in FIG. 15, the data may be analyzed and processed todetermine how to proceed. More specifically, data may be gathereddirectly from the workpiece, such as fabrication measurement dataindicative of a measurement associated with an attribute on theworkpiece, such as a particular layer that has been deposited or etched(block 1502). Such data is then directed to the active interdictioncontrol system of the common manufacturing platform. Additionally, andpossibly optionally, process parameter data and/or platform performancedata may be obtained by the active interdiction control system forfurther making decisions as disclosed herein. For example, certainprocess settings may be captured for the process that was performed justprior to measuring the workpiece. Furthermore, additional platformperformance data may be obtained to provide some indication of whether adetected non-conformity or defect is associated with the overallmanufacturing platform.

Once data has been measured and collected from other sources, such asfrom individual process control systems for a process module, or controlsystems for the manufacturing platform, the data may be analyzed andprocessed as set forth in step 1506. Such analysis and processing mayinclude a number of different algorithms, such as machine learningalgorithms including pattern recognition and correlation along with deeplearning and autonomous learning. Through such processing,non-conformities and defects might be detected as set forth in step1508. If no actionable non-conformities or defects are found in themeasurement/metrology process, the workpiece may proceed in the processsequence as normal. Alternatively, if such defects or non-conformitiesare detected and the active interdiction control system determines thatthey may be corrected or remediated, active interdiction of the processsequence takes place to provide corrective processing as in step 1510.If they cannot be corrected or remediated, they might be ejected fromthe process sequence.

Referring to FIG. 16, the active interdiction step may take a number ofdifferent paths. For example, if active interdiction is indicated by thecontrol system (step 1600) a remedial process (step 1602) may beperformed as a remedial process sequence in order to correct thenon-conformity. For example, the workpiece might be directed to anotherprocessing module in order to affect a particular layer to try andcorrect the non-conformity. For example, if the layer was deposited andwas not thick enough based upon the measurement step, the workpiecemight be returned to the previous process module or directed to anotherprocess module for further deposition. Alternatively, the remedialprocess sequence may inject a processing step through an etch module forremoving some of a layer that had been previously deposited.

Alternatively, if a non-conformity cannot be corrected, the activeinterdiction control system may direct the workpiece to an adjustmentprocess sequence to modify the non-conformity or defect that isdetected.

Still further, the active interdiction process 1600 might implement astep 1606 wherein process sequence parameters and various other processmodules are changed. For example, rather than providing the activeinterdiction on a current workpiece, subsequent workpieces might beaffected through changes in the steps or process parameters of aparticular process sequence. Such changes would be made in order toprevent any future non-conformities or defects that had been previouslydetected.

Finally, if remediation and adjustment to the workpiece are not suitableand the defects or non-conformities may not be overcome, the activeinterdiction may involve simply ejecting the workpiece from theprocessed sequence in order to not waste additional time and resourcesin processing the workpiece.

Returning to the flowchart of FIG. 14, if active interdiction isnecessary, it may be conducted is illustrated in step 1405.Alternatively, if active interdiction is not necessary the workpiecemade proceed in the process sequence as normal.

Following in the process sequence, in step 1406, the workpiece isoptionally transferred into a processing module for treating with atreatment gas. For example, the treatment gas can include an oxidizinggas or a reducing gas. In some examples, the oxidizing gas can include02, 1-120, 1-1202, isopropyl alcohol, or a combination thereof, and thereducing gas can include 1-12 gas. The oxidizing gas may be used tooxidize a surface of the first material layer 204 or the second material206 to improve subsequent area selective deposition. In one example, thetreatment gas can contain or consist of plasma-excited AR gas.

In the process, step 1406 might provide an additional juncture formeasurement and interdiction. In step 1408, the workpiece is optionallytransferred into a measurement module or TMM where the processing ortreatment of the workpiece in in step 1106 is measured andcharacterized. If active interdiction is indicated, it may be performedin step 1409.

Thereafter, the substrate is transferred into another processing modulewhere a self-aligned monolayer (SAM) is formed on the workpiece 1300 instep 1410. The SAM may be formed on the workpiece 1300 by exposure to areactant gas that contains a molecule that is capable of forming a SAMon the workpiece. The SAM is a molecular assembly that is formedspontaneously on substrate surfaces by adsorption and organized intomore or less large ordered domains. The SAM can include a molecule thatpossesses a head group, a tail group, and a functional end group, andthe SAM is created by the chemisorption of head groups onto theworkpiece from the vapor phase at room temperature or above roomtemperature, followed by a slow organization of the tail groups.Initially, at small molecular density on the surface, adsorbatemolecules form either a disordered mass of molecules or form an orderedtwo-dimensional “lying down phase”, and at higher molecular coverage,over a period of minutes to hours, begin to form three-dimensionalcrystalline or semi-crystalline structures on the substrate surface. Thehead groups assemble together on the substrate, while the tail groupsassemble far from the substrate.

According to one embodiment, the head group of the molecule forming theSAM can include a thiol, a silane, or a phosphonate. Examples of silanesinclude molecule that include C, H, Cl, F, and Si atoms, or C, H, Cl,and Si atoms. Nonlimiting examples of the molecule includeoctadecyltrichlorosilane, octadecylthiol, octadecyl phosphonic acic,perfluorodecyltrichlorosilane (CF₃(CF₂)₇CH₂CH₂SiCl₃),perfiuorodecanethiol (CF₃(CF₂)₇CH₂CH₂SH) chlorodecyldimethylsilane(CH₃(CH₂)₈CH₂Si(CH₃)₂Cl), and tertbutyl(chloro)dimethylsilane((CH3)3CSi(CH3)2Cl)).

The presence of the SAM on a workpiece 1300 may be used to enablesubsequent selective film deposition on the first material layer 1304(e.g., a dielectric layer) relative to the second material layer 1306(e.g., a metal layer). This selective deposition behavior is unexpectedand provides a new method for selectively depositing a film on the firstmaterial layer 1304 while preventing or reducing metal oxide depositionon the second material layer 1306. It is speculated that the SAM densityis greater on the second material layer 1306 relative to on the firstmaterial layer 1304, possibly due to higher initial ordering of themolecules on the second material layer 1306 relative to on the firstmaterial layer 1304. This greater SAM density on the second materiallayer 1306 is schematically shown as SAM 1308 in FIG. 13B.

Following the formation of the SAM 1308 on the workpiece, in step 1412,the workpiece is optionally transferred into a measurement module/TMMwhere the formation of the SAM 1308 on the workpiece is measured andcharacterized. If active interdiction is necessary, it may be performedin step 1413. The measurement system, for example, may make measurementsand collect data associated with the thickness, thickness non-uniformityand/or conformity. For example, as noted herein, poor selectivedeposition using the SAM layer may result if the surface coverage of theSAM layer is not sufficient in thickness or conformity. Also, if the SAMlayer is non-uniform, it may result in voids on the layer 1306. Througha measurement in a TMM/measurement module, such non-conformities mightbe detected. In such a case, the active interdiction control system maydirect the workpiece to an etch or cleaning module to remove the SAMlayer. For example, this might be done if it has a high level ofparticle contamination or the layer is not uniform or has incorrectdimensions. Alternatively, if not properly dimensioned, the SAM layermight be remediated, and the workpiece sent to a deposition chamber(e.g., back into the previous module) in order to put down more film ifthe layer is too thin. Alternatively, if the layer is too thick, theworkpiece may be sent to an etch module as part of the activeinterdiction or remediation.

Thereafter, the workpiece is transferred into another processing modulewhere, in step 1414, a film 1310 (e.g., a metal oxide film) isselectively deposited on the first material layer 1304 relative to onthe second material layer 1306 by exposing the workpiece 1300 to one ormore deposition gases. In one example, the film 1310 may include a metaloxide film that contains HfO₂, ZrO₂, or Al₂O₃. The film 1310 may, forexample, be deposited by CVD, plasma-enhanced CVD PEALD), ALD orplasma-enhanced ALD (PEALD). In some examples, the metal oxide film 1310may be deposited by ALD using alternating exposures of ametal-containing precursor and an oxidizer (e.g., 1-120, 1-1202,plasma-excited 02, or 03). During deposition of the film 1310, it isdesirable to maintain the selective deposition and deposit layer 1310only on layer 1304, but not on layer 1306, or even the SAM layer 1308.However, due to certain conditions, some deposition may occur on the SAMlayer. Thus, in accordance with the invention, upon completion of thedeposition layer 1310, measurements occur either in a TMM or othermeasurement module or measurement area, and active interdiction occursto address deposition on layer 1308.

As depicted in FIG. 13C, the exposure to the one or more depositiongases in the processing module may, in addition to depositing the film1310 on the dielectric layer 1304, also deposit film material, such asfilm nuclei 1312 on the SAM 1308. This loss of deposition selectivitycan occur if the deposition process is carried out for too long.Alternatively, the deposition selectivity between the dielectric layer1302 and the SAM 1308 may be poor. Poor deposition selectivity can alsooccur if the surface coverage of the SAM 1308 is incomplete and thelayer contains voids on the second material layer 1306.

Accordingly, following the deposition of the film 1310 on the workpiece,in step 1416, the workpiece is transferred into a measurement module/TMMwhere the deposition of the film 1310 is measured and characterized bythe active interdiction control system. The characterization candetermine the degree of deposition selectivity and if any activeinterdiction steps are necessary for the removal of the film nuclei 1312from the SAM 1308. If active interdiction is necessary, it may beperformed in step 1417, such as by directing the workpiece to an etchmodule.

The film nuclei 1312 on the SAM 1308 may be removed using an etchingprocess in order to selectively form the film 1310 on the first materiallayer 1304. The workpiece is transferred into another processing moduleto perform the etching process in step 1418. Although the film 1310 mayalso be partially removed by the etching process, the metal oxide nuclei1312 are expected to etch faster than the film 1310. The etching processcan include a dry etching process, a wet etching process, or acombination thereof. In one example, the etching process may include anatomic layer etching (ALE) process. The resulting workpiece shown inFIG. 13D has the film 1310 selectively formed on the first materiallayer 1304 with any film nuclei removal.

Following the etching process, in step 1420, the workpiece is optionallytransferred into a measurement module/TMM where the workpiece ismeasured and characterized so as to determine the results of theprocess. The characterization can determine the extent of the etchingprocess. If active interdiction is necessary, such as further etching,it may be performed in step 1421.

Thereafter, in step 1422, the SAM 1308 may be removed from theworkpiece, for example by etching or cleaning a process module or by aheat-treatment.

As schematically shown in FIG. 14, the above-described processing stepsmay be repeated one or more times to increase the thickness of the film1310 on the workpiece. Removal and subsequent repeated deposition of theSAM 1308 on the workpiece may be desired if the SAM 1308 becomes damagedduring the film deposition and/or the etching process and thereforeaffects the film deposition selectivity.

Unlike traditional metrology or process control in a manufacturingprocess, the workpiece does not leave the controlled environment toenter a stand-alone measurement/metrology tool thereby minimizingoxidation and defect generation, the measurements are non-destructivesuch that no workpiece is sacrificed to obtain data thereby maximizingproduction output, and the data can be collected in real time as part ofthe process flow to avoid negatively impacting production time and toenable in-process adjustments to the workpiece or to subsequentworkpieces being sequentially processed on the common manufacturingplatform. Additionally, the measurements are not performed in thefilm-forming or etching modules, thereby avoiding issues whenmeasurement devices are exposed to process fluids. For example, byincorporating workpiece measurement regions into the transfer module asin some of the disclosed embodiments, the data can be obtained as theworkpiece is traveling between processing tools with little to no delayin the process flow, without exposure to process fluids, and withoutleaving the controlled environment, e.g., without breaking vacuum. Whilethe “on the fly” data may not be as accurate as the data obtained fromtraditional destructive methods performed in stand-alone metrologytools, the nearly instantaneous feedback on the process flow and abilityto make real-time adjustment without interrupting the process flow orsacrificing yield is highly beneficial for high-volume manufacturing.

With further reference to the process flow 1430 of FIG. 14A, the methodmay include inspecting the workpiece, such as performing metrology,i.e., obtaining measurement data, using the active interdiction controlsystem at any of various times throughout the integrated method, withoutleaving the controlled environment, e.g., without breaking vacuum.Inspection or measurement of the workpiece may include characterizingone or more attributes of the workpiece and determining whether theattribute meets a target condition. For example, the inspection mayinclude obtaining measurement data related to an attribute anddetermining whether a defectivity, thickness, uniformity, and/orselectivity condition meets a target for that condition. The activeinterdiction control system may include one or moremeasurement/metrology modules or workpiece measurement region on thecommon manufacturing platform as discussed herein. The variousmeasurement/metrology operations and following active interdiction stepsmay optional at certain junctures, as indicated by the phantom lines inFIG. 14A for example but may be advantageously performed at one or morepoints in the process flow to ensure the workpiece is withinspecification. In one embodiment, measurement data is obtained aftereach step of the integrated sequence of processing steps conducted onthe common manufacturing platform. The measurement data may be used torepair the workpiece in one or more activeinterdiction/remediation/correction modules prior to leaving the commonmanufacturing platform, and/or may be used to alter parameters of theintegrated sequence of processing steps for subsequent steps and/or forsubsequent workpieces.

In broad terms, within the controlled environment, measurement data maybe obtained during the integrated sequence of processing steps relatedto the selective deposition of the additive material and, based on themeasurement data, a determination may be made whether defectivity,thickness, uniformity, and/or selectivity of the layer of additivematerial meets a target condition. When the defectivity, thickness,uniformity, and/or selectivity is determined to not meet the targetcondition, or an attribute of the workpiece is otherwise determined tobe non-conforming, the workpiece may be subjected to further activeinterdiction processing. For example, the workpiece may be processed inone or more modules that might be considered correction/remediationmodules on the common manufacturing platform to remove, minimize, orcompensate for the non-conforming attribute prior to performing a nextprocessing step in the integrated sequence of processing steps. Thecorrective action may include etching the target surface or non-targetsurface, depositing further additive material on the workpiece,repairing a barrier layer on the workpiece, thermally treating theworkpiece, or plasma treating the workpiece, for example. Other stepsmight also be part of the active interdiction depending on the detectednon-conformity or defect.

In one example, with processing using a SAM, the corrective action mayinclude removing the SAM when the non-conformity is based, at least inpart, on incomplete coverage or incomplete blocking of the non-targetsurface by the SAM or when an amount of exposed area of the non-targetsurface is greater than a predetermined exposed area threshold or whenan amount of additive material on the SAM surface is greater than apredetermined threshold. In another example, the corrective action mayinclude removing at least a portion of the layer of additive materialwhen the non-conformity is based, at least in part, on a step-heightdistance between the target surface and the non-target surface beingless than a predetermined step-height threshold or an amount of exposedarea of the non-target surface being less than the predetermined exposedarea threshold. In yet another example, the corrective action mayinclude adding further additive material to the workpiece when thenon-conformity is based, at least in part, on a thickness of theadditive material overlying the target surface being less than apredetermined thickness threshold. In a still further example, thecorrective action may include etching the workpiece when thenon-conformity is based, at least in part, on a remaining additivematerial on the non-target surface or a remaining self-assembledmonolayer on the non-target surface being greater than a predeterminedremaining thickness threshold. In another example, the corrective actionmay include thermally treating or plasma treating the workpiece when thenon-conforming workpiece attribute is based, at least in part, on areflectivity from the workpiece being less than a predeterminedreflectivity threshold.

The correction modules may be different film-forming and etching modulesthat are designated as correction modules on the common manufacturingplatform or another type of treatment module integrated on the commonmanufacturing platform, such as a thermal annealing module, or may bethe same film-forming and etching modules used to selectively depositthe additive material and etch the film nuclei.

The process flow 1430 of FIG. 14A will now be described in detail withthe optional inspection or metrology operations used to characterizeattributes of the workpiece to determine when a target thickness for theASD is reached and/or to determine if a non-conformality is present.Operation 1432 includes receiving a workpiece having the target andnon-target surfaces into a common manufacturing platform. Operation 1450includes optionally performing measurement/metrology to obtainmeasurement data related to attributes of the incoming workpiece, suchas attributes of the target surface and/or the non-target surface, whichmeasurement data may be used to adjust and/or control process parametersof any one of operations 1434-1438.

Operation 1434 includes optionally pre-treating the workpiece. Thepre-treatment may be a single operation or multiple operations executedon the common manufacturing platform. Operation 1452 includes optionallyperforming metrology to obtain measurement data related to attributes ofthe workpiece following the pre-treatment. If multiple pre-treatmentoperations are performed, the measurement data may be obtained after allpre-treatments are completed and/or after any individual pre-treatmentstep. In one example, the workpiece is inspected after a SAM is formedto determine whether the coverage is complete or if an exposed area ofthe treated surface exceeds a threshold value. The measurement data maybe used to adjust and/or control process parameters of any one ofoperations 1434-1438, may be used to make adjustments for subsequentworkpieces to the incoming attributes of the workpieces in operation1432 or to operation 1434, or may be used to repair the workpiece beforecontinued processing. In one embodiment, when the measurement dataindicates that one or more attributes do not meet a target condition,the workpiece may be transferred to a correction module to repair theworkpiece. For example, when coverage by a SAM on the non-target surfaceis incomplete, corrective action may be taken in one or more processingmodules, such as removing the SAM and reapplying the SAM.

Operation 1436 includes selectively depositing additive material on theworkpiece in a film-forming module hosted on the common manufacturingplatform. Operation 1454 includes optionally performing metrology toobtain measurement data related to attributes of the workpiece havingthe layer of additive material formed on the target surface, such asattributes of the layer of additive material, the non-target surface,and/or a pre-treated surface as affected by the selective deposition,which measurement data may be used to adjust and/or control processparameters of any one of operations 1438-1442, may be used to makeadjustments for subsequent workpieces to the incoming attributes of theworkpieces in operation 1432 or to operations 1434-1436, or may be usedto repair the workpiece before continued processing. In one embodiment,when the measurement data indicates that one or more attributes do notmeet a target condition, the workpiece may be transferred to acorrection module to repair the layer of additive material or thenon-target surface. For example, when the defectivity, thickness,uniformity, or selectivity of the additive material does not meet atarget condition, corrective action may be taken in one or morecorrection modules, such as by selectively depositing additionaladditive material onto the target surface, removing additive materialfrom the non-target surface or target surface, removing a pre-treatmentlayer from the non-target surface, thermally treating or plasma treatingthe workpiece, or a combination of two or more thereof.

Operation 1438 includes etching the workpiece using an etching modulehosted on the common manufacturing platform to expose the non-targetsurface. Operation 1438 may include etching film nuclei that depositedon the non-target surface or on a SAM formed on the non-target surfaceor etching a complete layer of additive material deposited on thenon-target surface or on a SAM formed on the non-target surface at athickness less than the thickness of the layer of additive materialformed on the target surface. Operation 1438 may also include removing aSAM or other pre-treatment layer from the non-target surface, either inthe same etching step or a subsequent etching step. Operation 1456includes optionally performing measurement/metrology to obtainmeasurement data related to attributes of the workpiece having the layerof additive material on the target surface and the etched non-targetsurface, such as attributes of the layer of additive material asaffected by the etching, attributes of the non-target surface exposed bythe etching, and/or attributes of a SAM or other pre-treatment layer asaffected by etching the film nuclei from the SAM on the non-targetsurface, which measurement data may be used to adjust and/or controlprocess parameters of any one of operations, including steps 1434-1438in the repetition of the sequence per operation 1442, may be used tomake adjustments for subsequent workpieces to the incoming attributes ofthe workpieces in operation 1432 or to operations 1434-1438, or may beused to repair the workpiece before continued processing. In oneembodiment, when the measurement data indicates that one or moreattributes do not meet a target condition, the workpiece may betransferred to a correction module to the layer of additive material orthe non-target surface. For example, when the defectivity, thickness,uniformity, or selectivity of the additive material does not meet atarget condition, corrective action may be taken in one or morecorrection modules, such as by selectively depositing additionaladditive material onto the target surface, removing additive materialfrom the non-target surface or target surface, removing a pre-treatmentlayer from the non-target surface, thermally treating or plasma treatingthe workpiece, or a combination of two or more thereof. Further, whenthe measurement data indicates that the thickness of the layer ofadditive material is less than a target thickness, such thatdetermination 1440 is No, the workpiece may be subjected to repeatingsteps of the sequence per operation 1442. When the measurement dataindicates that the thickness of the layer of additive material hasreached the target thickness, such that determination 1440 is Yes, theworkpiece may exit the common manufacturing platform.

Process parameters, as referred to above, may include any operatingvariable within a processing module, such as but not limited to: gasflow rates; compositions of etchants, deposition reactants, purge gases,etc.; chamber pressure; temperature; electrode spacing; power; etc. Theintelligence system of the active interdiction system is configured togather measurement data from the inspection system and control theintegrated sequence of processing steps executed on the commonmanufacturing platform, for example, by making in situ adjustments toprocessing parameters in subsequent processing modules for the workpiecein process, or by changing process parameters in one or more processingmodules for subsequent workpieces. Thus, the obtained measurement datamay be used to identify a needed repair to the workpiece during theintegrated sequence of processing steps to avoid having to scrap theworkpiece, and/or to adjust processing parameters for the integratedsequence of processing steps for steps performed on the same workpieceafter the measurement data is obtained or for processing subsequentworkpieces to reduce occurrences of the target conditions not being metfor the subsequent workpieces.

While some of the illustrated examples indicate and ASD layer of metaloxide film on a dielectric layer, the present invention can apply aswell to metal-on-metal (MoM) selective deposition ordielectric-on-dielectric (DoD) selective deposition.

The invention might also be implemented for active interdiction with aself-aligned multi-patterning process as done on the inventive system.In such a scenario, as noted herein, the active interdiction system mayone or more measurement/metrology modules or workpiece measurementregions on the common manufacturing platform. Various measurement ormetrology operations may be optionally performed, as indicated in FIG.14B, but may be advantageously performed at one or more points in theprocess flow to ensure the workpiece is within specification to reducedefectivity and EPE. In one embodiment, measurement data is obtainedafter each step of the integrated sequence of processing steps conductedon the common manufacturing platform. The measurement data may be usedto initiate active interdiction and repair the workpiece in aremediation or correction module prior to leaving the commonmanufacturing platform, and/or may be used to alter parameters of theintegrated sequence of processing steps for subsequent workpieces.

For multi-patterning processes, for example, within the controlledenvironment, measurement data may be obtained during the integratedsequence of processing steps related to the formation of the sidewallspacer pattern and, based on the measurement data. For example, aTMM/measurement module or a measurement region in the common platformmay provide data regarding the thickness, width, or profile of thesidewall spacer pattern and the data may be analyzed by the interdictioncontrol system to determine whether a measured thickness, width, orprofile of the sidewall spacer pattern meets a target condition. Whenthe thickness, width, or profile of the sidewall spacer pattern isdetermined to not meet the target condition, active interdiction may benecessary, and the workpiece may be processed in a processing module onthe common manufacturing platform to alter the sidewall spacer pattern.In one embodiment, when the target thickness, width, or profile of thesidewall spacer pattern is not met, the sidewall spacer pattern may berepaired. In one example, the workpiece might be passed to afilm-formation module for selectively depositing additional materialonto a structure. Alternatively, a process module might be used forconformally depositing additional material onto a structure. Stillfurther, the active interdiction may be using one or process modules toreshaping a structure, etching a structure, implant dopant into astructure, removing and reapply a material layer of a structure. Alsovarious of the remediation correction steps might be combined for theproper active interdiction as directed by the control system.

In an embodiment, when a conformality or uniformity of a thin filmapplied in a film-forming module on the common manufacturing platformdoes not meet a target conformality or target uniformity for the thinfilm, corrective or active interdiction action may be taken to repairthe thin film. In one example, repairing a conformally applied thin filmmay be accomplished by removing the thin film and reapplying the thinfilm. As such, the workpiece may be passed to one or more etch and/orcleaning process modules and then to a film-formation module to reapplythe film. In another active interdiction example, the workpiece might goto a film-formation module for conformally applying an additional thinfilm or to an etch module for etching the thin film, or some combinationof film-formation and etch. For example, the workpiece may betransferred to a correction etching module to remove the thin film orpartially etch the thin film, and/or the workpiece may be transferred toa correction film-forming module to reapply the thin film after it isremoved or to apply additional thin film over the existing thin film orpartially etched thin film.

In an embodiment, when the thickness, width, or profile of the sidewallspacers formed in an etching module on the common manufacturing platformdoes not meet a target thickness, width, or profile of the sidewallspacers, corrective action may be taken to repair the sidewall spacers.Repairing sidewall spacers may be accomplished by selectively depositingadditional material onto the sidewall spacers, reshaping the sidewallspacers, implanting dopant into the sidewall spacers, or a combinationof two or more thereof. For example, the workpiece may be transferred toa correction film-forming module to selectively deposit spacer materialor to one or more correction film-forming and/or etching modules toperform a sidewall spacer reshaping process.

The correction modules may be different film-forming and etching modulesthat are designated as correction/remediation modules on the commonmanufacturing platform or another type of treatment module integrated onthe common manufacturing platform, such as a thermal annealing module.Alternatively, the modules used in active interdiction may be the samefilm-forming and etching modules used to conformally apply the thinfilm, etch the thin film, and remove the mandrel pattern.

The process flow 1460 of FIG. 14B will now be described in detail withthe optional metrology operations. Operation 1462 includes receiving aworkpiece having a first mandrel pattern into a common manufacturingplatform. Operation 1480 includes optionally performingmeasurement/metrology to obtain measurement data related to attributesof the incoming workpiece, such as attributes of the first mandrelpattern and/or an underlying layer over which the mandrel pattern isformed and into which the final pattern is to be transferred. Themeasurement data may be used to adjust and/or control process parametersof any one of operations 1464-1478.

Operation 1464 includes conformally applying a first thin film over thefirst mandrel pattern using a film-forming module hosted on the commonmanufacturing platform. Operation 1482 includes optionally performingmeasurement/metrology to obtain measurement data related to attributesof the workpiece having the conformal first thin film applied, such asattributes of the first thin film, the first mandrel pattern as affectedby the thin film deposition, and/or the underlying layer into which thefinal pattern is to be transferred as affected by the thin filmdeposition, which measurement data may be used to adjust and/or controlprocess parameters of any one of operations 1464-1468, may be used tomake adjustments for subsequent workpieces to the incoming attributes inoperation 1462 or to operation 1464, or may be used to repair theworkpiece before continued processing. In one embodiment, when themeasurement data indicates that one or more attributes do not meet atarget condition, the workpiece may be transferred to a process moduleto repair the conformally applied first thin film. For example, when aconformality or uniformity of the first thin film does not meet a targetconformality or target uniformity for the first thin film, correctiveaction may be taken in one or more process modules, such as removing thethin film and reapplying the thin film, conformally applying anadditional thin film, etching the thin film, or a combination of two ormore thereof.

Operation 1466 includes removing the first thin film from upper surfacesof the first mandrel pattern and lower surfaces adjacent the firstmandrel pattern (e.g., from the underlying layer) using an etchingmodule hosted on the common manufacturing platform to form firstsidewall spacers (referred to as a spacer etch). Operation 1484 includesoptionally performing measurement/metrology to obtain measurement datarelated to attributes of the workpiece having the etched first thin filmforming first sidewall spacers on the sidewalls of the first mandrelpattern, such as attributes of the first sidewall spacers, the firstmandrel pattern as affected by the spacer etch, and/or the underlyinglayer as affected by the spacer etch, which measurement data may be usedto adjust and/or control process parameters of any one of operations1468-1478, may be used to make adjustments for subsequent workpieces tothe incoming attributes of the workpieces in operation 1462 or tooperations 1464-1466, or may be used to repair the workpiece beforecontinued processing. In one embodiment, when the measurement dataindicates that one or more attributes do not meet a target condition,the workpiece may be transferred to a correction module to repair thefirst sidewall spacers on the sidewalls of the mandrel pattern. Forexample, when the thickness, width, or profile of the sidewall spacersdoes not meet a target thickness, width, or profile of the sidewallspacers, corrective action may be taken in one or more process modules,such as by selectively depositing additional material onto the sidewallspacers, reshaping the sidewall spacers, implanting dopant into thesidewall spacers, or a combination of two or more thereof.

Operation 1468 includes removing the first mandrel pattern (referred toas a mandrel pull) using an etching module hosted on the commonmanufacturing platform to leave behind the first sidewall spacers.Operation 1486 includes optionally performing measurement/metrology toobtain measurement data related to attributes of the workpiece havingthe first sidewall spacers, such as attributes of the first sidewallspacers as affected by the mandrel pull and/or the underlying layer asaffected by the mandrel pull, which measurement data may be used toadjust and/or control process parameters of any one of operations1470-1478, may be used to make adjustments for subsequent workpieces tothe incoming attributes of the workpieces in operation 1462 or tooperations 1464-1468, or may be used to repair the workpiece beforecontinued processing. In one embodiment, when the measurement dataindicates that one or more attributes do not meet a target condition,the workpiece may be transferred to a correction module to repair thefirst sidewall spacers. For example, when the thickness, width, orprofile of the sidewall spacers does not meet a target thickness, width,or profile of the sidewall spacers, corrective action may be taken inone or more process modules, such as by selectively depositingadditional material onto the sidewall spacers, reshaping the sidewallspacers, implanting dopant into the sidewall spacers, or a combinationof two or more thereof.

In a self-aligned double patterning embodiment, process flow 1460 mayproceed to operation 1478, discussed below, via flow 1470, eitherwithout or after operation 1486. Operation 1472 includes conformallyapplying a second thin film over the first sidewall spacers that serveas a second mandrel pattern, using a film-forming module hosted on thecommon manufacturing platform. Operation 1488 includes optionallyperforming measurement/metrology to obtain measurement data related toattributes of the workpiece having the conformal second thin filmapplied, such as attributes of the second thin film, the second mandrelpattern as affected by the thin film deposition, and/or the underlyinglayer as affected by the thin film deposition, which measurement datamay be used to adjust and/or control process parameters of any one ofoperations 1474-1478, may be used to make adjustments for subsequentworkpieces to the incoming attributes of the workpieces in operation1462 or to operations 1464-1468 or may be used to repair the workpiecebefore continued processing. In one embodiment, when the measurementdata indicates that one or more attributes do not meet a targetcondition, the workpiece may be transferred to a correction module torepair the conformally applied second thin film. For example, when aconformality or uniformity of the second thin film does not meet atarget conformality or target uniformity for the second thin film,corrective action may be taken in one or more process modules, such asremoving the thin film and reapplying the thin film, conformallyapplying an additional thin film, etching the thin film, or acombination of two or more thereof.

Operation 1474 includes removing the second thin film from uppersurfaces of the second mandrel pattern and lower surfaces adjacent thesecond mandrel pattern (e.g., from the underlying layer) using anetching module hosted on the common manufacturing platform to formsecond sidewall spacers (referred to as a spacer etch). Operation 1490includes optionally performing measurement/metrology to obtainmeasurement data related to attributes of the workpiece having theetched second thin film forming second sidewall spacers on the sidewallsof the second mandrel pattern, such as attributes of the second sidewallspacers, the second mandrel pattern as affected by the spacer etch,and/or the underlying layer as affected by the spacer etch, whichmeasurement data may be used to adjust and/or control process parametersof any one of operations 1476-1478, may be used to make adjustments forsubsequent workpieces to the incoming attributes of the workpieces inoperation 1462 or to operations 1464-1474 or may be used to repair theworkpiece before continued processing. In one embodiment, when themeasurement data indicates that one or more attributes do not meet atarget condition, the workpiece may be transferred to a process moduleto repair the second sidewall spacers on the sidewalls of the secondmandrel pattern. For example, when the thickness, width, or profile ofthe sidewall spacers does not meet a target thickness, width, or profileof the sidewall spacers, corrective action may be taken in one or moreprocess modules, such as by selectively depositing additional materialonto the sidewall spacers, reshaping the sidewall spacers, implantingdopant into the sidewall spacers, or a combination of two or morethereof.

Operation 1476 includes removing the second mandrel pattern (referred toas a mandrel pull) using an etching module hosted on the commonmanufacturing platform, to leave behind the second sidewall spacers.Operation 1492 includes optionally performing measurement/metrology toobtain measurement data related to attributes of the workpiece havingthe second sidewall spacers, such as attributes of the second sidewallspacers as affected by the mandrel pull and/or the underlying layer asaffected by the mandrel pull, which measurement data may be used toadjust and/or control process parameters of operation 1478, may be usedto make adjustments for subsequent workpieces to the incoming attributesof the workpieces in operation 1462 or to operations 1464-1476, or maybe used to repair the workpiece before continued processing. In oneembodiment, when the measurement data indicates that one or moreattributes do not meet a target condition, the workpiece may betransferred to a process module to repair the second sidewall spacers.For example, when the thickness, width, or profile of the sidewallspacers does not meet a target thickness, width, or profile of thesidewall spacers, corrective action may be taken in one or more processmodules, such as by selectively depositing additional material onto thesidewall spacers, reshaping the sidewall spacers, implanting dopant intothe sidewall spacers, or a combination of two or more thereof.

Process parameters, as referred to above, may include any operatingvariable within a processing module, such as but not limited to: gasflow rates; compositions of etchants, deposition reactants, purge gases,etc.; chamber pressure; temperature; electrode spacing; power; etc. Theintelligence system of the active interdiction system is configured togather measurement data from the inspection system and control theintegrated sequence of processing steps executed on the commonmanufacturing platform, for example, by making in situ adjustments toprocessing parameters in subsequent processing modules for the workpiecein process, or by changing process parameters in one or more processingmodules for subsequent workpieces. Thus, the obtained measurement datamay be used to identify a needed active interdiction step or repair tothe workpiece during the integrated sequence of processing steps toavoid having to scrap the workpiece, and/or to adjust processingparameters for the integrated sequence of processing steps for stepsperformed on the same workpiece after the measurement data is obtainedor for processing subsequent workpieces to reduce occurrences of thetarget conditions not being met for the subsequent workpieces.

Active interdiction might also be implemented in contact formationprocesses. Contact formation on a workpiece can be implemented on thecommon manufacturing platform. In one embodiment, contacts may be formedusing a patterned mask layer to selectively expose transistor contactareas to a plurality of processes (e.g., clean, metal deposition,anneal, metal etch). In another embodiment, contacts may be formed usingselective deposition and etch processes to apply and remove metal fromthe transistor contact areas without using a patterned mask layer.

In a patterned mask layer embodiment, the common manufacturing platformmay receive a workpiece having one or more contact features formed andexposed through a patterned mask layer. The contact feature has asemiconductor contact surface exposed at a bottom of the contactfeature, the semiconductor contact surface containing silicon, orgermanium, or alloy thereof. The common manufacturing platform may begintreating the semiconductor contact surface in one of the one or moreetching modules to remove contamination therefrom. In one embodiment, anX-ray photo-emission spectroscopy measurement may be conducted on theincoming wafer prior to the treatment to detect the level contaminationwithin the contact feature. Alternatively ellipsometry (e.g., thicknessmeasurement) may be done to determine or approximate the amount of oxideon the semiconductor contact surface. In doing so, the commonmanufacturing platform may optimize the treatment process to removematerial in the etch module.

Following the treatment, the contamination and thickness measurementsmay be done again to confirm the contamination or oxide layer has beenadequately removed. If not, the common manufacturing platform and activeinterdiction control system thereof may take remedial action by treatingthe workpiece a one or more additional times through the etch module.This measurement and treatment process may be repeated until thecontamination or oxide is below a predetermined threshold level. In someinstances, a high-resolution optical measurement systems may be used inthe TMM/measurement module (e.g, high-resolution optical imaging andmicroscopy, hyperspectral (multi-spectral) imaging, interferometry,spectroscopy, Fourier transform Infrared spectroscopy (FTIR)reflectometry, scatterometry, spectroscopic ellipsometry, polarimetry,refractometers or non-optical imaging systems (e.g., SEM, TEM, AFM) tomeasure the dimensions of the contact feature

Next, the common manufacturing platform moves the workpiece to a metaldeposition module to deposit a metal layer within the contact feature onthe semiconductor contact surface. A measurement system of the a TMM ormeasurement module may measure the film properties of a deposited layer(e.g., thickness, resistance, uniformity, conformality) using one ormore measurement/metrology systems (e.g., optical or non-opticaltechniques) incorporated into the common manufacturing platform. Basedon the measurement and/or process performance data, the activeinterdiction control system may implement a remedial action on theworkpiece to increase or decrease metal layer thickness and will movethe workpiece as appropriate to a film-formation module or etch moduleto achieve the desired result based on the measurements. Alternatively,control system may move the workpiece appropriately to remove the metallayer and reapply a second metal to replace the first metal layer. Inthis instance, the metal layer is in physical contact with thedielectric material of one or more transistor components, for example.

Although the metal layer is physically contacting the dielectricmaterial of the transistor, the contact is not yet fully formed becausethe interface resistance between the metal and dielectric material istoo high with the abrupt transition between metal and dielectricmaterial. One approach to reduce the resistance is to anneal or heat theworkpiece to form a metal-dielectric alloy, wherein the resistance ofthe alloy is lower than the dielectric material and higher than themetal. Following the heat treatment, the active interdiction controlsystem may move the workpiece to measure the resistance, using a filmresistivity metrology system, to confirm alloy formation is withinpredetermined limits. In this instance, the active interdiction controlsystem may also determine that an additional heat treatment is needed tofully form the alloy material to achieve the desired resistance andworkpiece transfer mechanisms in the common manufacturing platform areoperated accordingly for such a step.

Following the heat treatment, the workpiece may be moved to an etchmodule to remove the unalloyed portion of the metal layer to expose thealloy within the contact feature. Again, the active interdiction controlsystem may position to workpiece with a TMM or measurement module orsome other measurement system to measure the resistance to determinewhether an unalloyed portion of the metal layer has been adequatelyremoved. The etch process may be repeated by the active interdictioncontrol system until the aforementioned condition is achieved. However,in some embodiments, the metal layer may be entirely consumed as aresult of the alloy treatment. In this instance, the metal etch processmay not be needed.

In some embodiments, the patterned-mask layer process may includeapplying a conductive capping layer on the deposited metal layer or thealloyed layer in one of the one or more film-forming modules to cap themetal layer or alloy layer to prevent metal oxide or othercontamination.

In other embodiments, the common manufacturing platform may beconfigured and controlled to form via structures (e.g., W, Co, Ru) abovethe contact to connect the contact to the metal lines, formed laterabove the transistor, which provide the electrical signals to thetransistor components.

In another embodiment, contact formation may be implemented using areaselective deposition (ASD) techniques which rely on chemical propertiesof exposed materials on the workpiece and deposited films to selectivelyinteract with each other, such that the deposited films only grow oncertain exposed material or grow at a much higher rate. Hence, thepatterned mask layer may be omitted from the incoming workpiece.However, the ASD embodiment still uses many of the same steps as thepatterned mask layer embodiment, with two primary differences. Theapplication and removal of the self-assembled monolayer, wherein the SAMis applied before metal deposition and removed after metal deposition.The SAM layer replaces the patterned mask layer enables the blanketmetal deposition to selectively deposit on the contact features. Forexample, in the mask embodiment, the metal layer deposits on the contactfeature and the mask layer to form a blanket layer of metal over theworkpiece. In contrast, in the ASD embodiment, the metal is selectivelydeposited on the contact features which are not covered by the SAM layerand does not form a metal layer on the SAM that has the same metal layerthickness over the contact features.

In the ASD embodiment, the common manufacturing platform and the activeinterdiction control system will use various measurement/metrologysystems to confirm the SAM coverage and/or density adequately covers thenon-contact features on the workpiece and/or exposes the contactfeatures on the workpiece. Likewise, the active interdiction controlsystem and common manufacturing platform can use measurement/metrologysystems to determine that the SAM material is adequately removed fromthe workpiece. The metrology systems may include high-resolution optical(e.g, high-resolution optical imaging and microscopy), hyperspectral(multi-spectral) imaging, interferometry, spectroscopy, Fouriertransform Infrared spectroscopy (FTIR) reflectometry, scatterometry,spectroscopic ellipsometry, polarimetry, or refractometers.

Autonomous Learning Engine

The subject innovation is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present innovation.

As used in the subject specification, the terms “object,” “module,”“interface,” “component,” “system,” “platform,” “engine,” “unit,”“store,” and the like are intended to refer to a computer-related entityor an entity related to an operational machine with a specificfunctionality, the entity can be either hardware, a combination ofhardware and software, software, or software in execution. For example,a component may be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and/or a computer. By way of illustration, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. Also, these components canexecute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal).

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Referring to the drawings, FIG. 17 illustrates an example autonomousbiologically based learning system 1700 that might be implemented by anactive interdiction control system. An adaptive inference engine 1710 iscoupled to a goal component 1720. A wired or wireless communication link1715 couples such components. For a specific goal established or pursuedby goal component 1720, adaptive inference component 1710 receives aninput 1730 such as measurement data, process parameter data, platformperformance data as captured herein that can be employed to accomplishthe goal and conveys output 1740 that can represent or record aspects ofthe pursued or accomplished goal. In addition, adaptive inference engine1710 can receive data from a data store 1750 through link 1755 and canstore data or information in such data store, e.g., stored informationcan be a portion of output 1740 that is conveyed through a wired orwireless link 1765. It should be appreciated that (i) input 1730, output1740, and data in data store 1750 (as well as the history of input,output, and data in the data store) comprise a context for the operationof adaptive inference engine 1710, and (ii) a feedback of that contextinto the engine via links 1715, 1755, and 1765 facilitates adaptationbased on context. In particular, goal component 1720 can exploit fedback context to adapt a specific, initial goal and thus establish andpursue the adapted goal.

Input 1730 can be regarded as extrinsic data or information, which caninclude measurement module data, inspection system data, processingmodule parameter data, platform performance data, etc from the commonmanufacturing platform as well as process sequence data. This data caninclude instructions, records, results of measurements; and so on.Output 1740 can be substantially the same in nature as input 1730, andit can be regarded as intrinsic data. Input and output can be receivedand conveyed, respectively, by input and output interfaces andconnections with the manufacturing platform (e.g., USB ports, IRwireless inputs), that can reside in adaptive inference component 1710.As indicated above, input 1730 and output 1740 can be a portion of acontext for adaptive inference engine 1710. Additionally, adaptiveinference component 1710 can request input 1730 as a result of pursuinga goal.

Components in autonomous biologically based system 1700 can be definedrecursively, which can confer the autonomous system 1700 a substantialdegree of competent learning complexity with basic elementarycomponents.

Each link 1715, 1755, or 1765 can include a communication interface thatcan facilitate manipulation of data or information to be transmitted orreceived; can utilize databases for data storage and data mining; andcan receive and convey information from and to an actor. Wiredembodiments of links 1715, 1755, or 1765 can include a twisted-pairline, a T1/E1 phone line, an AC line, an optical fiber line, andcorresponding circuitry, whereas wireless embodiments can comprise anultra-mobile wide band link, a long-term evolution link, or an IEEE802.11 link, and associated electronics. Regarding data store 1750,although it is illustrated as a single element, it can be a distributeddata warehouse, wherein set of data memories are deployed in disparatephysical or logical locations.

In example system 1700, the adaptive inference engine 1710 and the goalcomponent 1720 are illustrated as separate components, however, itshould be appreciated that one of such components can reside within theother.

Goal component 1720 can belong to one or more disciplines (e.g., ascientific discipline such as semiconductor manufacturing or enterprisesectors related to semiconductor manufacturing (e.g., a market sector,an industry sector, a research sector and so on). Additionally, as goalscan typically be multidisciplinary and focus on multiple markets, a goalcomponent can establish multiple disparate goals within one or moreparticular disciplines or sectors. To pursue a goal, a goal componentcan comprise a functional component and a monitor component. Specificoperations to accomplish a goal are affected through the functionalcomponent(s), whereas conditions of variables related to theaccomplishment of the goal are determined by the monitor component.Additionally, the functional component(s) can determine a space of goalsthat can be accomplished by the goal component 1720. A space of goalscomprises substantially all goals that can be attained with a specificfunctionality. It should be appreciated that, for such specificfunctionality afforded by a functional component, a contextualadaptation of a specific goal can adapt a first goal to a second goalwithin a space of goals. An initial goal within a space of goals can bedetermined by one or more actors; wherein an actor can be a machine or ahuman agent (e.g., an end user). It should be noted that an initial goalcan be a generic, high-level objective, as the adaptation inferenceengine 1710 can drive goal component 1720 towards a complex detailedobjective through goal drifting. Goals, goal components, and goaladaptation are illustrated next.

FIG. 18 is a diagram 1800 that delineates contextual goal adaptation. Agoal (e.g., goal 1810 ₁, or goal 1810 ₃) can typically be an abstractionthat is associated with the functionality of a goal component (e.g.,component 1720). A goal can be a high level abstraction: “Save forretirement,” “secure a profit,” “be entertained,” “learn to cook,” “totravel to a locale,” “develop a database,” “manufacture a product,” andso on. Additionally, goals can be more specific refinements such as“save to retire early with an annual income in the range of$60,000-$80,000,” “travel from the United States to Japan in low season,with travel costs including housing not to exceed $5000,” or “reach ajob interview site to deliver a 35 minute presentation to a group ofassociates of the prospective employer.” Furthermore, a goal (e.g., 1810₁) possesses an associated context (e.g., 1820 ₁). As indicated above,goal component 1720 coupled to adaptive inference engine 1710 generallyis compatible with an established goal (e.g., goal 1810 ₁, or goal 1810₃). For instance, the goal “manufacture a product” (e.g., goal 1810 ₁)can rely on a manufacturing tool system such as a molecular beam epitaxyreactor (an example goal component 1720) that adopts standard or customspecifications to manufacture the product. During the accomplishment ofsuch a goal (e.g., goal 1810 ₁), output 1740 can include themanufactured product. In addition, an adaptive inference component(e.g., component 1710) can adapt (e.g., adaptation 1830 ₁) the“manufacture a product” goal (e.g., goal 1810 ₁) based on context (e.g.,context 1820 ₁) like the one that can be generated by tool systemspecifications or data gathered by a monitor component in the goalcomponent. In particular, the initial high-level goal (e.g., goal 1810₁) can be adapted to “manufacture a semiconductor device” (e.g., goal1810 ₂). As indicated above, a goal component 1720 can be composed ofmultiple functional components in order to accomplish a goal.Additionally, goal component 1720 can be modular, wherein goalsub-component can be incorporated as a goal is adapted. As an example, agoal component that pursues the “manufacture a product” goal cancomprise a multi-market evaluation and forecast component that iscoupled to a massively parallel, intelligent computing platform whichcan analyze market conditions in various markets in order to adapt(e.g., 1830 ₁) the goal to “manufacture a multicore-processor thatutilizes molecular electronics components” (e.g., goal 1810 _(N)). Itshould be noted that such an adaptation can involve a number ofintermediate adaptations 1830 ₁-1830 _(N-1), as well as intermediateadapted goals 1810 ₂-1810_(N-1) wherein intermediated adaptation isbased on intermediate contexts 1820 ₂-1820 _(N) generated from apreviously pursued goal.

In another illustration of goal, goal component and goal adaptation, agoal can be to “purchase a DVD of movie A at store B,” the goalcomponent 1720 can be a vehicle with a navigation system that comprisesan adaptive inference engine 1710. (It should be noted that in thisillustration the adaptive inference engine 1710 resides in the goalcomponent 1720.) An actor (e.g., a vehicle operator) can enter or selectthe location of store B and goal component can generate directions toaccomplish the goal. In the instance that the adaptive inference engine1710 receives input 1730 that store B has ceased to carry in inventorymovie A (e.g., an RFID reader has updated an inventory database and anupdate message has been broadcasted to component 1710) while the actoris traveling to the store, adaptive inference engine 1710 can (i)request additional input 1730 to identify a store C with movie A instock, (ii) evaluate the resources available to the actor to reach storeC, and (iii) assess the level of interest of the actor in accomplishingthe goal. Based on the modified context developed through input 1730 asillustrated in (i)-(iii), goal component can receive an indication toadapt the goal “to purchase a DVD of movie A at store C.”

It should be appreciated that adaptive inference engine 1710 canestablish sub-goals associated with a goal determined by goal component1720. A sub-goal can facilitate accomplishing the goal by enablingadaptive inference engine to accomplish complementary task or to learnconcepts associated with the goal.

As a summary, autonomous biologically based system 1700 is a goal-drivensystem with contextual goal-adaptation. It should be appreciated thatgoal adaptation based on received context introduces an additional layerof adaptation to the analysis of input information to generateactionable information output 1740. The capabilities of (a) adapting theprocess of information or data analysis and (b) adapting an initial goalbased on context render the system massively adaptive or autonomous.

FIG. 19 illustrates a high level block diagram of an example autonomousbiologically based learning tool 1900. In embodiment 1900, theautonomous learning system includes a tool system 1910 that comprises afunctional component 1915 which confers the tool system its specificfunctionality and can comprise a single functional tool component or acollection of substantially identical or diverse functional toolcomponents, and a sensor component 1925 that can probe severalobservable magnitudes related to a process performed by the tool, like athermal treatment of a semiconductor wafer, and generates assets 1928associated with the process. Collected assets 1928, which include dataassets such as production process data or test run data, can be conveyedto an interaction component 1930 which includes an adaptor component1935 that can serve as an interface to receive assets 1928, aninteraction manager 1945 which can process the received assets 1928, anddatabase(s) 1955 that can store the received and processed data.Interaction component 1930 facilitates interaction of tool system 1910with autonomous biologically based learning system 1960. Informationassociated with the data generated in the process performed bymanufacturing platform tool system 1910 which can be received andincrementally supplied to autonomous learning system 1960. For example,measurement data associated with workpieces, as well as processingparameter data associated with process modules of the platform isdirected to the interaction component 1930.

Autonomous biologically based learning system 1960 includes a memoryplatform 1365 that stores received information 1958 (e.g., data,variables and associated relationships, causal graphs, templates, and soon) which can be communicated via a knowledge network 1975 to aprocessing platform 1985 that can operate on the received informationand can communicate back a processed information through the knowledgenetwork 1975 to the memory platform 1965. The constituent components ofautonomous learning system 1960 can generally resemble biologicalaspects of the brain, in which a memory is networked with processingcomponents to manipulate information and generate knowledge.Additionally, knowledge network 1975 can receive information from, andconvey information to, interaction component 1930, which can communicatethe information to tool system 1910, or an actor 1990 via interactionmanager 1945. As information 1958 is received, stored, processed andconveyed by the autonomous learning system 1960, multiple improvementscan be effected in tool system 1910 and actors that rely on it. Namely,improvements include (a) the autonomous learning system 1960 and toolsystem 1910 become increasingly independent as time progresses, andrequire lesser actor intervention (e.g., human direction andsupervision), (b) the autonomous system improves the quality of itsoutputs to actors (for example, better identification of root causes offailures, or prediction of system failure before occurrence thereof),and (c) the autonomous learning system 1960 improves its performanceover time—the autonomous system 1960 delivers improved results at afaster rate and with fewer resources consumed.

Memory platform 1965 comprises a hierarchy of functional memorycomponents, which can be configured to store knowledge (e.g.,information 1958) received during initialization or configuration oftool system 1910 (e.g., a priori knowledge). A priori knowledge can beconveyed as information input 1958 through the interaction component1930. In addition, memory platform 1965 can store (a) training data(e.g., information input 1958) employed to train the autonomous learningsystem 1960 after initialization/configuration of tool system 1910, and(b) knowledge generated by the autonomous learning system 1960; theknowledge can be conveyed to tool system 1910 or actor 1990 throughinteraction component 1930, via interaction manager 1945.

Information input 1958 (e.g., data) supplied by an actor 1990, e.g., ahuman agent, can comprise data identifying a variable associated with aprocess, a relationship between two or more variables, a causal graph(e.g., a dependency graph), or an episode information. Such informationcan facilitate to guide the autonomous biologically based system 1960 ina learning process. Additionally, in one aspect, such information input1958 can be deemed important by actor 1990, and the importance can berelated to the relevance of the information to a specific processperformed by tool system 1910. For instance, an operator (e.g., actor1990 is a human agent) of an oxide etch system can determine that etchrate is critical to the outcome of the manufacturing process; thus, etchrate can be an attribute communicated to autonomous learning system1960. In another aspect, information input 1958 supplied by actor 1990can be a hint, whereby an indication to learn a particular relationshipamong process variables is made. As an example, hint can convey asuggestion to learn the behavior of pressure in a deposition chamber intool system 1910, within a specific deposition step, as a function ofchamber volume, exhaust pressure and incoming gas flow. As anotherexample, a hint can indicate to learn a detailed temporal relationshipfor a chamber pressure. Such example hints can activate one or morefunctional processing units in the autonomous learning system that canlearn the functional dependence of pressure on multiple processvariables. Moreover, such hints can activate one or more functionalunits that can apply and compare a learnt functionality with respect tomodel or empirical functionalities available to actor 1990.

A tool system 1910, e.g., a semiconductor manufacturing tool, can becomplex and therefore disparate actors can specialize in manipulatingand operating the tool system through disparate types of specific,complete or incomplete knowledge. As an example, a human agent, e.g., atool engineer can know that different gases have different molecularweight and thus can produce different pressures, whereas a process/toolengineer can know how to convert a pressure reading resulting from afirst gas to an equivalent pressure resulting from a second gas; anelementary example of such knowledge can be to convert a pressurereading from a unit (e.g., Pa) to another (e.g., Ib/in², or PSI). Anadditional type of general, more complex knowledge present in theautonomous biologically based learning system can be functionalrelationships between properties of a tool system (e.g., volume of achamber) and measurements performed in the tool system (e.g., measuredpressure in the chamber). For example, etch-engineers know that the etchrate is dependent on the temperature in the etch chamber. To allow forthe diversity of knowledge and the fact that such knowledge can beincomplete, an actor (e.g., a human agent such as an end-user) can guidean autonomous learning system 1960 through multiple degrees of conveyedknowledge: (i) No knowledge specified. Actor delivers no guidance forthe autonomous learning system. (ii) Basic knowledge. Actor can convey avalid relationship between properties of a tool system and measurementsin the tool system; for instance, actor conveys a relationship (e.g.,relationship (K_(E),T)) between an etch rate (K_(E)) and processtemperature (T) without further detail. (iii) Basic knowledge withidentified output. Further to a relationship between a tool systemproperty and a tool system measurement, actor can provide specificoutput for a dependent variable in a relationship (e.g.,relationship(output(K_(E)), T). (iv) partial knowledge about arelationship. Actor knows the structure of a mathematical equation amonga tool system property and a measurement, as well as relevant dependentand independent variables (e.g., K_(E)=k₁e^(−k2/T) without concretevalues for k₁ or k₂). The actor 1990, however, can fail to know aprecise value of one for more associated constants of the relationship.(v) Complete knowledge. Actor possesses a complete mathematicaldescription of a functional relationship. It should be noted that suchguidance can be incrementally provided over time, as the autonomouslearning system 1960 evolves and attempts to learn tool functionalrelationships autonomously.

Knowledge network 1975 is a knowledge bus that communicates information(e.g., data) or transfers power according to an established priority.The priority can be established by a pair of information source andinformation destination components or platforms. Additionally, prioritycan be based on the information being transmitted (e.g., thisinformation must be dispatched in real-time). It should be noted thatpriorities can be dynamic instead of static and change as a function oflearning development in the autonomous learning system 1960, and in viewof one or more demands in the one or more components present in theautonomous biologically based learning tool 1900—e.g., a problemsituation can be recognized, and a communication can be warranted andeffected in response. Communication, and power transfer, via knowledgenetwork 1975 can be effected over a wired link (e.g., a twisted pairlink, a T1/E1 phone line, an AC line, an optical fiber line) or awireless link (e.g., UMB, LTE, IEEE 802.11), and can occur amongcomponents (not shown) within a functional platform (e.g., memoryplatform 1965 and processing platform 1985) or among components indisparate platforms (e.g., a component in memory platform ofself-awareness communicating with another sub-component ofself-awareness) or the communication can be between components (e.g., acomponent of awareness communicates with a component inconceptualization).

Processing platform 1985 comprises functional processing units thatoperate on information: Input information of a specific type (e.g.,specific data types such as a number, a sequence, a time sequence, afunction, a class, a causal graph, and so on) is received or retrievedand a computation is performed by a processing unit to generate outputinformation of a specific type. Output information can be conveyed toone or more components in memory platform 1965 via knowledge network1975. In an aspect, the functional processing units can read and modifydata structures, or data type instance, stored in memory platform 1965,and can deposit new data structures therein. In another aspect,functional processing units can provide adjustments to various numericattributes like suitability, importance, activation/inhibition energy,and communication priority. Each functional processing unit has adynamic priority, which determines a hierarchy for operating oninformation; higher priority units operate on data earlier than lowerpriority units. In case a functional processing unit that has operatedon specific information fails to generate new knowledge (e.g., learn),like generating a ranking number or ranking function that distinguishesa bad run from a good run associated with operation of a tool system1910, the priority associated with the functional processing unit can belowered. Conversely, if new knowledge is generated, the processingunit's priority is increased.

It should be appreciated that processing platform 1985, throughprioritized functional processing units, emulates a human tendency toattempt a first operation in a specific situation (e.g., a specific datatype), if the operation generates new knowledge, the operation isexploited in a subsequent substantially identical situation. Conversely,when the first operation fails to produce new knowledge, a tendency toemploy the first operation to handle the situation is reduced and asecond operation is utilized (e.g., spread activation). If the secondoperation fails to generate new knowledge, its priority is reduced, anda third operation is employed. Processing platform 1985 continues toemploy an operation until new knowledge is generated, and otheroperation(s) acquire higher priority.

In an aspect, actor 1990 can provide process recipe parameters,instructions (e.g., a temperature profile for an annealing cycle of anion implanted wafer, a shutter open/close sequence in a vapor depositionof a semiconductor, an energy of an ion beam in an ion implantationprocess, or an electric field magnitude in a sputtering deposition), aswell as initialization parameters for the autonomous learning system1960. In another aspect, an actor 1990 can supply data associated withmaintenance of tool system 1910. In yet another aspect, actor 1990 cangenerate and provide results of a computer simulation of the processperformed by tool system 1910. Results generated in such a simulationcan be employed as training data to train the autonomous biologicallybased learning system. Additionally, a simulation or an end-user candeliver optimization data associated with a process to tool system 1910.

Autonomous learning system 1960 can be trained through one or moretraining cycles, each training cycle can be utilized to develop theautonomous biologically based learning tool 1900 to (i) be able toperform a larger number of functions without external intervention; (ii)provide better response such as improved accuracy, or correctness, whendiagnosing root cause of manufacturing system health root causes; and(iii) increase performance such as faster response time, reduced memoryconsumption, or improved quality of product. Training data can besupplied to the autonomous learning system 1960 via adaptor component1935, in case training data is collected from data 1928 associated witha process calibration or standard run in tool system 1910—such data canbe deemed to be internal—or through interaction manager 1945. Whentraining data is retrieved from database(s) 1965 (e.g., data related toexternal measurements conducted through an external probe, or records ofrepair intervention in tool system 1910); such training data can bedeemed external. When training data is supplied by an actor, data isconveyed through interaction manager 1945 and can be deemed external. Atraining cycle based on internal or external training data facilitatesautonomous learning system 1960 to learn an expected behavior of toolsystem 1910.

As indicated above, functional component 1915 can comprise multiplefunctional tool components (not shown) associated with the tool specificsemiconductor manufacturing capabilities of a manufacturing platform asdescribed herein and that enable the tool to be used to (a) manufacturesemiconductor substrates (e.g., wafers, flat panels, liquid crystaldisplays (LCDs), and so forth), (b) conduct epitaxial vapor depositionor non-epitaxial vapor deposition, (c) facilitate ion implantation orgas cluster ion infusion, (d) perform a plasma or non-plasma (dry orwet) an oxide etch treatment, (e) implement a lithographic process(e.g., photo-lithography, e-beam lithography, etc.), and so on. The toolsystem 1910 can also be embodied in a furnace; an exposure tool foroperation in a controlled electrochemical environment; a planarizationdevice; an electroplating system; measurement module or inspectionsystem device for optical, electrical, and thermal properties, which caninclude lifespan (through operation cycling) measurements; variousmeasurement and metrology modules, a wafer cleaning machine, and thelike.

In the process conducted by tool system 1910, sensors and probescomprising sensor component 1925 of an inspection system can collectdata (e.g., data assets) associated with an attribute of a workpiece asdescribed and on different physical properties of process modules (e.g.,pressure, temperature, humidity, mass density, deposition rate, layerthickness, surface roughness, crystalline orientation, dopingconcentration, etc.) as well as mechanical properties of process modulesand the manufacturing platform (valve aperture or valve angle, shutteron/off operation, gas flux, substrate angular velocity, substrateorientation, and the like) through various transducers and techniqueswith varying degrees of complexity depending on the intended use of thegathered data. Such techniques can include but are not limited to thevarious measurement and metrology techniques as described herein toobtain the noted data for detecting non-conformities and defects andproviding active interdiction. It should be appreciated that the sensorsand measurement module inspection systems provide the data from the toolsystem. It should also be appreciated that such data assets 1928effectively characterize the measured data from the workpiecesmanufactured or fabricated by the manufacturing platform of the toolsystem 1910.

In an aspect, data sources in sensor component or inspection system 1925can be coupled to adaptor component 1935, which can be configured togather data assets 1928 in analog or digital form. Adaptor component1935 can facilitate data 1968 collected in a process run to be composedor decomposed according to the intended utilization of the data inautonomous learning system 1960 before the data is deposited into memoryplatform 1965. Adaptors in adaptor component 1935 can be associated withone or more sensors in sensor component/inspection systems 1925 and canread the data from the one or more sensors. An external data sourceadapter may have the ability to pull data as well as pass through datathat is pushed from outside the tool. For example, an MES/historicaldatabase adaptor knows how to consult an MES database to extractinformation for various autobots and package/deposit the data intoworking memory for one or more components of the autonomous system. Asan example, adaptor component 1935 can gather wafer-level run data oneworkpiece or wafer at a time as the tool processes the workpiece. Then,adaptor component 1935 can consolidate individual runs in a batch toform “lot-level-data,” “maintenance-interval-data”, etc. Alternatively,if tool system 1910 outputs a single file (or computer product asset)for lot-level data, adaptor component 1935 can extract wafer-level data,step-level data, and the like. Furthermore, decomposed data elements canrelate to one or more components of tool system 1900; e.g., variablesand times at which a pressure controller in sensor component 1925 isoperating. Subsequent to processing, or packaging, received data 1928 asdescribed above, adaptor component 1935 can store processed data indatabase(s) 1955.

Database(s) 1955 can include data originated in (i) tool system 1910,through measurements performed by sensors in the inspectionsystem/sensor component 1925, (ii) a manufacturing execution system(MES) database or a historical database, or (iii) data generated in acomputer simulation of tool system 1910, e.g., a simulation ofsemiconductor wafer manufacturing performed by actor 1990. In an aspect,an MES is a system that can measure and control a manufacturing processand process sequence, can track equipment availability and status, cancontrol inventory, and can monitor for alarms.

It is to be appreciated that products, or product assets, fabricated bytool system 1910 can be conveyed to actor 1990 through interactioncomponent 1930. It should be appreciated that product assets can beanalyzed by actor 1990 and the resulting information, or data assets,conveyed to autonomous learning system 1960. In another aspect,interaction component 1930 can perform analysis of a product asset 1928via adaptor component 1935.

In addition, it is to be noted that in embodiment 1900 the interactioncomponent 1930 and autonomous learning system 1960 are externallydeployed with respect to tool system 1910. Alternative deploymentconfigurations of autonomous biologically based learning tool 1900 canbe realized, such as embedded deployment wherein interaction component1930 and autonomous biologically based learning system 1960 can residewithin the manufacturing platform tool system 1910, in a single specifictool component; e.g., single embedded mode, or in a cluster of toolcomponents of the platform; e.g., multiple embedded mode. Suchdeployment alternatives can be realized in a hierarchical manner,wherein an autonomous learning system supports a set of autonomouslearning tools that form a group tool or platform, or a toolconglomerate. Such complex configurations are discussed in detail below.

Next, an illustrative tool system 2000 is discussed in connection withFIG. 20, and an example architecture for the autonomous biologicallybased learning system 1960 is presented and discussed in detail withrespect to FIGS. 21-25.

FIG. 21 illustrates a high level block diagram of example architecture2100 of an autonomous biologically based learning system. In embodiment2100, autonomous learning system 1960 comprises a hierarchy offunctional memory components that include a long term memory (LTM) 2110,a short term memory (STM) 2120, and an episodic memory (EM) 2130. Eachof such functional memory components can communicate through knowledgenetwork 1975, which operates as described in discussed in connectionwith FIG. 19. In addition, autonomous learning system 1960 can includean autobot component 2140 that includes functional processing unitsidentified as autobots, with substantially the same characteristics asthose functional units described in connection with processing platform1985. It is to be noted that that autobot component 2140 can be a partof processing platform 1985.

Furthermore, autonomous learning system 1960 can comprise one or moreprimary functional units which include a self-awareness component 2150,a self-conceptualization component 2160, and a self-optimizing component2170. A first feed forward (FF) loop 2152 can act as a forward link andcan communicate data among self-awareness component 2150 andself-conceptualization 2160. In addition, a first feedback (FB) loop2158 can act as a reverse link and can communicate data amongself-conceptualization component 2170 and self-awareness component 2150.Similarly, forward link and reverse link data communication amongself-conceptualization component 2160 and self-optimization component2170 can be accomplished, respectively, through a second FF loop 2162and a second FB loop 2168. It should be appreciated that in an FF link,data can be transformed prior to communication to the component thatreceives the data to further process it, whereas in a FB link a nextdata element can be transformed by the component that receives the dataprior to process it. For example, data transferred through FF link 2152can be transformed by self-awareness component 2150 prior tocommunication of the data to self-conceptualizing component 2160. Itshould further be appreciated that FF links 2152 and 2162 can facilitateindirect communication of data among components 2150 and 2170, whereasFB links 2168 and 2158 can facilitate an indirect communication of dataamong components 2170 and 2150. Additionally, data can be conveyeddirectly among components 2150, 2160, and 2170 through knowledge network1975.

Long term memory 2110 can store knowledge supplied through interactioncomponent 1930 during initialization or configuration of a tool system(e.g., a priori knowledge) to train the autonomous learning tool system1900 after initialization/configuration. In addition, knowledgegenerated by autonomous learning system 1960 can be stored in long termmemory 2110. It should be appreciated that LTM 2110 can be a part of amemory platform 1965 and thus can display substantially the samecharacteristics thereof. Long term memory 2110 can generally comprise aknowledge base that contains information about manufacturing platformcomponents (e.g., processing modules, measurement modules, inspectionsystems, transfer modules and so on), relationships, processing stepsand procedures. At least a portion of the knowledge base can be asemantic network that describes or classifies data types (for example asa sequence, an average, or a standard deviation), relationships amongthe data types, and procedures to transform a first set of data typesinto a second set of data types.

A knowledge base may contain knowledge elements, or concepts. In anaspect, each knowledge element can be associated with two numericattributes: a suitability (ξ) and an inertia (ι) of a knowledge element,or concept; collectively such attributes determine a priority of aconcept. A well-defined function, e.g., a weighted sum, a geometricaverage, of these two numeric attributes can be a concept's situationscore (σ). For example, σ=ξ+ι. The suitability of a knowledge elementcan be defined as a relevance of the knowledge element (e.g., concept)to a tool system or a goal component situation at a specific time. In anaspect, a first element, or concept, with a higher suitability scorethan a second element can be more relevant to a current state of theautonomous learning system 1960 and a current state of a tool system1910 than the second element with a lower suitability score. The inertiaof a knowledge element, or concept, can be defined as the difficultyassociated with utilization of the knowledge element. For example, a lowfirst value of inertia can be conferred to a number element, a list ofnumbers can be attributed a second inertia value higher than the firstvalue, a sequence of numbers can have a third value of inertia that ishigher than the second value, and a matrix of numbers can have a fourthvalue of inertia which can be higher than the third value. It is notedthat inertia can be applied to other knowledge or information structureslike graphs, tables in a database, audio files, video frames, codesnippets, code scripts, and so forth; the latter items can substantiallyall be a portion of input 1730. The subject innovation provides for awell-defined function of the suitability and the inertia that caninfluence the likelihood that a knowledge element is retrieved andapplied. Concepts that have the highest situational score are the mostlikely concepts to be rendered to short term memory 2120 for processingby processing units.

Short term memory 2120 is a temporary storage that can be utilized as aworking memory (e.g., a workspace or cache) or as a location wherecooperating/competing operations, or autobots, associated with specificalgorithms or procedures, can operate on data types. Data contained inSTM 2120 can possess one or more data structures. Such data structuresin STM 2120 can change as a result of data transformations effected byautobots and planner überbots (e.g., autobots dedicated to planning).The short term memory 2120 can comprise data, learning instructionsprovided by the interaction manager 1945, knowledge from the long termmemory 2110, data provided and/or generated by one or more autobots orüberbots, and/or initialization/configuration commands provided by anactor 1990. Short term memory 2120 can track a state of one or moreautobots and/or überbots used to transform data stored therein.

Episodic memory 2130 stores episodes which can include anactor-identified set of parameters and concepts which can be associatedwith a process. In an aspect, an episode can comprise extrinsic data orinput 1730, and it can provide with a specific context to autonomouslearning system 1900. It is noted that an episode can generally beassociated with a particular scenario identified or generated (e.g., bytool system 1910, a goal component 1720, or an autonomous learningsystem 1960) while pursuing a goal. An actor that identifies an episodecan be a human agent, like a process engineer, a tool engineer, a fieldsupport engineer, and so on, or it can be a machine. It should beappreciated that episodic memory 2130 resembles a human episodic memory,wherein knowledge associated with particular scenario(s)—e.g., anepisode—can be present and accessible without a recollection of thelearning process that resulted in the episode. Introduction, ordefinition, of an episode typically is a part of a training cycle orsubstantially any extrinsic provision of input, and it can lead to anattempt by the autonomous biologically based learning system 1960 tolearn to characterize data patterns, or input patterns, that can bepresent in data associated with the episode. A characterized pattern ofdata associated with an episode can be stored in episodic memory 2130 inconjunction with the episode and an episode's name. The addition of anepisode to episodic memory 2130 can result in a creation of anepisode-specific autobot that can become active when a set of parametersin a process conducted by a tool system 1910, or a generally a goalcomponent 1720, enter an operating range as defined in the episode; theepisode-specific autobot receives sufficient activation energy when thefirst feature associated with a pursued goal or process is recognized.If the parameters meet the criteria established through a receivedepisode, the episode-specific autobot compares the pattern of data inthe episode with the current data available. If the current situation(as defined by the recognized pattern of data) of the tool system 1910,or a goal component, matches the stored episode, an alarm is generatedto ensure the tool maintenance engineers can become aware of thesituation and can take preventive action(s) to mitigate additionaldamage to functional component 1915 or sensor component 1925 or materialutilized in a tool process.

Autobot component 2140 comprises a library of autobots that perform aspecific operation on an input data type (e.g., a matrix, a vector, asequence, and so on). In an aspect, autobots exist in an autobotsemantic net, wherein each autobot can have an associated priority; apriority of an autobot is a function of its activation energy (E_(A))and its inhibition energy (E_(I)). Autobot component 2140 is anorganized repository of autobots that can include autobots for theself-awareness component 2150, self-conceptualization component 2160,self-optimization component 2170, and additional autobots that canparticipate in transforming and passing data among components and amongthe various memory units. Specific operations that can be performed byan autobot can include a sequence average; a sequence ordering; a scalarproduct among a first and a second vector; a multiplication of a firstmatrix and a second matrix; a time sequence derivative with respect totime; a sequence autocorrelation computation; a cross-correlationoperation between a first and a second sequence; a decomposition of afunction in a complete set of basic functions; a wavelet decompositionof a time sequence numeric data stream, or a Fourier decomposition of atime sequence. It should be appreciated that additional operations canbe performed depending on input data; namely, feature extraction in animage, sound record, or biometric indicator, video frame compression,digitization of environment sounds or voice commands, and so on. Each ofthe operations performed by an autobot can be a named function thattransforms one or more input data types to produce one or more outputdata types. Each function for which there exists an autobot in autobotcomponent 2140 can possess an element in LTM, so that itherbots can makeautobot activation/inhibition energy decisions based on the total“attention span” and needs of the autonomous learning system 1960.Analogously to the autonomous learning system 1960, an autobot inautobot component 2140 can improve its performance over time.Improvements in an autobot can include better quality of producedresults (e.g., outputs), better execution performance (e.g., shorterruntime, capability to perform larger computations, and the like), orenhanced scope of input domain for a particular autobot (e.g., inclusionof additional data types that the autobot can operate on).

Knowledge—concepts and data—stored in LTM 2110, STM 2120 and EM 2130 canbe employed by primary functional units, which confer autonomousbiologically based learning system 1960 a portion of its functionality.

Self-awareness component 2150 can determine a level of tool systemdegradation between a first acceptable operating state of the toolsystem 1910 and a subsequent state, at a later time, in which toolsystem has degraded. In an aspect, autonomous learning system 1960 canreceive data that characterizes an acceptable operating state, and dataassociated with a product asset such as a workpiece fabricated in suchacceptable state; such data assets can be identified as canonical data.Autonomous biologically based learning system 1960 can process thecanonical data and the associated results (e.g., statistics aboutimportant parameters, data regarding non-conformities and defects in aworkpiece observed drift in one or more measured attributes orparameters of a workpiece, predictive functions relating toolparameters, and so on) can be stored by self-awareness component 2150and employed for comparison to data supplied as information input 1958;e.g., production process data or test run data or patterns on aworkpiece. If a difference between generated, learnt results of thecanonical data and the device process run-data or patterns is small,then the manufacturing system degradation can be considered to be low.Alternatively, if the difference between stored learnt results of thecanonical data and the sample process data or other workpiece data islarge, then there can be a significant level of non-conformities ordefects in the workpiece. A significant level of non-conformities andprocess degradation can lead to a process, or goal, contextualadjustment. Degradation as described herein can be computed from adegradation vector (Q₁, Q₂, . . . , Q_(U)) where each component Q_(λ)(λ=1, 2, . . . , U) of the degradation vector is a different perspectiveof an available data set—e.g., Q₁ may be a multivariate mean, Q₂ theassociated multivariate deviation, Q₃ a set of wavelet coefficients fora particular variable in a process step, Q₄ may be the mean differencebetween a predicted pressure and measured pressure, etc. Normal trainingruns produce a specific set of values (e.g., a training data asset) foreach component, which can be compared with component Q₁-Q_(U) generatedwith run data (e.g., a run data asset) from each component. To assessdegradation, a suitable distance metric can be employed to compare the(e.g., Euclidean) distance of a run degradation vector from its “normalposition” in {Q} space; the large such Euclidean distance, the more atool system is said to be degraded. In addition, a second metric can beto compute a cosine similarity metric among the two vectors.

Self-conceptualization component 2160 can be configured to build anunderstanding of important manufacturing platform and tool system 1910relationships (e.g., one or more process chamber behavior functions) anddescriptions (e.g., statistics regarding requested and measuredparameters, influence of parameters on degradation, etc.). It is to beappreciated that relationships and descriptions are also data, or soft,assets. The understanding is established autonomously (e.g., byinference and contextual goal adaptation originated from input data;inference can be accomplished, for example, via multivariate regressionor evolutionary programming, such as genetic algorithms) by autonomouslearning system 1960, or through an actor 1990 (e.g., a human agent)supplied guidance. Self-conceptualization component 2160 can construct afunctional description of a behavior of a single parameter of a toolsystem 1910, or generally a goal component like component 1720, such aspressure in a film-forming module in a semiconductor manufacturingsystem as a function of time during a specific deposition step. Inaddition, self-conceptualization component 2160 can learn a behaviorassociated with a tool system, like a functional relationship of adependent variable on a specific set of input information 1958. In anaspect, self-conceptualization component 2160 can learn the behavior ofpressure in a deposition chamber of a given volume, in the presence of aspecific gas flow, a temperature, exhaust valve angle, time, and thelike. Moreover, self-conceptualization component 2160 can generatesystem relationships and properties that may be used for predictionpurposes. Among learnt behaviors, self-conceptualization component 2160can learn relationships and descriptions that characterize a normalstate. Such normal state typically is employed by autonomous learningsystem 1960 as a reference state with respect to which variation inobserver tool behavior is compared.

Self-optimization component 2170 can analyze a current health orperformance of an autonomous biologically based learning system 1900based on the level of a tool system 1910 deviation between predictedvalues (e.g., predictions based on functional dependence orrelationships learnt by self-conceptualization component 2160 andmeasured values) in order to identify (a) a potential cause of anon-conformity from the manufacturing platform/tool system 1960, or (b)one or more sources of root cause of the manufacturing platform/toolsystem degradation based on information gathered by autonomous learningsystem 1960. Self-optimizing component 2170 can learn over time whetherautonomous learning system 1960 initially incorrectly identifies anerroneous root cause for a non-conformity or defect, the learning system1900 allows for input of maintenance logs or user guidance to correctlyidentify an actual root cause. In an aspect, the autonomous learningsystem 1960 updates a basis for its diagnosis utilizing Bayesianinference with learning to improve future diagnosis accuracy.Alternatively, optimization plans can be adapted, and such adapted planscan be stored in an optimization case history for subsequent retrieval,adoption, and execution. Moreover, a set of adaptations to a processconducted by tool system 1910, or generally a goal pursued by a goalcomponent 1720, can be attained through the optimization plans.Self-optimization component 2170 can exploit data feedback (e.g., loopeffected through links 1965, 1955, and 1915) in order to develop anadaptation plan that can promote process or goal optimization.

In embodiment 2100, autonomous biologically based learning system 1960can further comprise a planner component 2180 and a system contextcomponent 2190. The hierarchy of functional memory components 2110,2120, and 2130, and the primary functional units 2150, 2160, and 2170can communicate with planner component 2180 and the system contextcomponent 2190 through knowledge network 1975.

Planner component 2180 can exploit, and comprise, higher level autobotsin autobot component 2140. Such autobots can be identified as plannerüberbots and can implement adjustments to various numeric attributeslike a suitability, an importance, an activation/inhibition energy, anda communication priority. Planner component 2180 can implement a rigid,direct global strategy; for instance, by creating a set of plannerüberbots that can force specific data types, or data structures, to bemanipulated in short term memory 2120 through specific knowledgeavailable in short term memory 2120 and specific autobots. In an aspect,autobots created by planner component 2180 can be deposited in autobotcomponent 2140 and be utilized over the knowledge network 1975.Alternatively, or in addition, planner component 2180 can implement anindirect global strategy as a function of a current context of anautonomous learning system 1960, a current condition of a tool system1910, a content of short term memory 2120 (which can include associatedautobots that can operate in the content), and a utilizationcost/benefit analysis of various autobots. It should be appreciated thatthe subject autonomous biologically based learning tool 1900 can afforddynamic extension of planner components.

Planner component 2180 can act as a regulatory component that can ensureprocess, or goal, adaptation in an autonomous biologically based tool1900 does not result in degradation thereof. In an aspect, regulatoryfeatures can be implemented through a direct global strategy viacreation of regulatory überbots that infer operational conditions basedon planned process, or goal, adaptation. Such an inference can beeffected through a semantic network of data types on which theregulatory überbots act, and the inference can be supported orcomplemented by cost/benefit analysis. It should be appreciated thatplanner component 2180 can preserve goals drifting within a specificregion of a space of goals that can mitigate specific damages to a goalcomponent, e.g., a tool system 1910.

System context component 2190 can capture the current competency of anautonomous biologically based learning tool 1900 that exploitsautonomous learning system 1960. System context component 2190 caninclude a state identifier that comprises (i) a value associated with aninternal degree of competency (e.g., a degree of effectiveness of amanufacturing platform/tool system 1910 in conducting a process (orpursuing a goal), a set of resources employed while conducting theprocess, a quality assessment of a final product or service (or anoutcome of a pursued goal), a time-to-delivery of devices, and so on),and (ii) a label, or identifier, to indicate the state of the autonomouslearning tool 1900. For instance, the label can indicate states such as“initial state,” “training state,” “monitoring state,” “learning state,”or “applying knowledge.” The degree of competency can be characterizedby a numerical value, or metric, in a determined range. Further, thesystem context component 2190 can include a summary of learningperformed by the autonomous learning system 1960 over a specific timeinterval, as well as a summary of possible process or goal adaptationsthat can be implemented in view of the performed learning.

FIG. 22A illustrates an example autobot component 2140. Autobots 2215₁-2215 _(N) represent a library of autobots and überbots, each withspecific dynamics priority 2225 ₁-2225 _(N). Autobots 2215 ₁-2215 _(N)can communicate with a memory (e.g., a long term or short term memory,or an episodic memory). As indicated supra, an autobot's priority, is adetermined by the autobot's activation energy and inhibition energy. Anautobot (e.g., autobot 2215 ₁, or 2215 _(N)) gains activation energy(through überbots) when data that can be processed by the autobot is inSTM. A weighted sum of an autobot (e.g., autobot 2215 ₂) activationenergy and inhibition energy, e.g., I=w_(A)E_(A)+w_(I)E_(I) candetermine when the autobot can activate itself to perform its functionaltask: The autobot self-activate when Σ>ψ, where ψ is a predetermined,inbuilt threshold. It should be appreciated that the subject autonomousbiologically based learning tool 1900 can afford dynamic augmentation ofautobots.

FIG. 22B illustrates an example architecture 2250 of an autobot. Theautobot 2260 can be substantially any of the autobots included in anautobot component 2140. A functionality component 2263 determines andexecutes at least a portion of an operation that autobot 2260 canperform on input data. Processor 2266 can execute at least a portion ofthe operation performed by the autobot 2260. In an aspect, processor2266 can operate as a co-processor of functionality component 2263.Autobot 2260 can also comprise an internal memory 2269 in which a set ofresults of previously performed operations. In an aspect, internalmemory operates as a cache memory that stores input data associated withan operation, current and former values of E_(A) and E_(I), a log of thehistory of operation of the autobot, and so on. Internal memory 2269 canalso facilitate autobot 2260 to learn how to improve quality offorthcoming results when a specific type and quantity of error is fedback or back propagated to the autobot 2260. Therefore, autobot 2260 canbe trained through a set of training cycles to manipulate specific inputdata in a specific manner.

An autobot (e.g., autobot 2260) can also be self-describing in that theautobot can specify (a) one or more types of input data the autobot canmanipulate or require, (b) a type of data the autobot can generate, and(c) one or more constraints on input and output information. In anaspect, interface 2275 can facilitate autobot 2260 to self-describe andthus express the autobot's availability and capability to überbots, inorder for the überbots to supply activation/inhibition energy to theautobots according to a specific tool scenario.

FIG. 23 illustrates example architecture 2300 of a self-awarenesscomponent in an autonomous biologically based learning system 1960.Self-awareness component 2150 can determine a current level ofdegradation with respect to a learned normal state in a manufacturingplatform/tool system (e.g., tool system 1910). Non-conformities in aworkpiece and degradation can arise from multiple sources such aswear-and-tear or mechanical parts in the tool system; improper operationor developmental operation to develop recipes (e.g., a data asset) orprocesses that can force the manufacturing platform/tool system tooperate outside one or more optimal ranges; improper customization ofthe manufacturing platform/tool system; or inadequate adherence tomaintenance schedules. Self-awareness component 2150 can be recursivelyassembled, or defined, through (i) a hierarchy of memories, e.g.,awareness memories which can be part of memory platform 1965, (ii)functional operational units such as awareness autobots that can residein an autobot component 2140 and be a part of processing platform 1985,and (iii) a set of awareness planners. Based on the level ofdegradation, autonomous learning system 1960 can analyze available dataassets 1928 as well as information 1958 to rank the possible faults. Inan aspect, in response to an excessive level of non-conformities theautonomous learning system can provide control for corrective processingthrough the platform. In case of a successful corrective processing asconfirmed, for example, by further measurement/metrology and associateddata (e.g., data assets and patterns, relationships, and substantiallyany other type of understanding extracted from such combination) thatpreceded the corrective processing activities can be retained byautonomous learning system 1960. Thus, in forthcoming instances in whichlearned symptoms are identified through new understanding autonomouslygleaned from data assets, and analysis, the manufacturing platform andthe process sequence may be adapted to prevent further non-conformities.

Awareness working memory (AWM) 2310 is a S™ that can include a specialregion of memory identified as awareness sensory memory (ASM) 2320 thatcan be utilized to store data, e.g., information input 1958, that canoriginate in a sensor in sensor component 1925 or in actor 1990, can bepackaged by one or more adaptors in adaptor component 1935, and can bereceived by knowledge network 1975. Self-awareness component 2150 canalso comprise multiple special functionality autobots, which can residein autobot component 2140 and include awareness planner überbots (APs).

In addition, self-awareness component 2150 can comprise an awarenessknowledge memory (AKM) 2330 which is a part of a LTM and can includemultiple concepts—e.g., an attribute; an entity such as a class or acausal graph; a relationship, or a procedure-relevant to the operationof self-awareness component 2150. In an aspect, a self-awarenesscomponent 2150 for a semiconductor manufacturing platform tool caninclude domain specific concepts like a step, a run, a batch, amaintenance-interval, a wet-clean-cycle, etc., as well as generalpurpose concepts like a number, a list, a sequence, a set, a matrix, alink, and so on. Such concepts can enter a higher level of abstraction;for instance, a workpiece run can be defined as an ordered sequence ofprocess steps where a step has both recipe parameter settings (e.g.,desired values), and one or more step measurements. Furthermore, AKM2330 can include functional relationships that can link two or moreconcepts like an average, a standard deviation, a range, a correlation,a principal component analysis (PCA), a multi-scale principal componentanalysis (MSPCA), a wavelet or substantially any basis function, etc. Itshould be noted that multiple functional relationships can beapplicable, and hence related, to a same concept; for example, a list ofnumbers is mapped to a real number instance by the average, which is a(functional) relation and a standard-deviation relation, as well as amaximum relation, and so forth). When a relationship from one or moreentities to another entity is a function or a functional (e.g., afunction of a function), there can be an associated procedure that canbe executed by an überbot in order to effect the function. A precisedefinition of a concept can be expressed in a suitable data schemadefinition language, such as UML, OMGL, etc. It should be furthernoticed that a content of AKM 2330 can be augmented dynamically at (toolsystem) runtime without shutting the system down.

Each concept in AKM 2330, as any concept in a knowledge base asdescribed herein, can be associated with a suitability attribute and aninertia attribute, leading to the concept's specific situation score.Initially, before the autonomous system is provided with data, thesuitability value for all elements in AKM 2330 is zero, but the inertiafor all concepts can be tool dependent and can be assigned by an actor,or based on historical data (e.g., data in database(s) 1955). In anaspect, inertia of a procedure that produces an average from a set ofnumbers can be substantially low (e.g., t=1) because computation of anaverage can be regarded as a significantly simple operation that can beapplicable to substantially all situations involved collected data sets,or results from computer simulations. Similarly, maximize and minimizeprocedures, which transform a set of numbers, can be conferred asubstantially low inertia value. Alternatively, compute a range andcompute a standard deviation can be afforded higher inertia values(e.g., t=2) because such knowledge elements are more difficult to apply,whereas calculate a PCA can display a higher level of inertia andcalculate a MSPCA can have a yet higher value of inertia.

A situation score can be employed to determine which concept(s) tocommunicate among from AKM 2330 and AWM 2310 (see below). Knowledgeelements, or concepts, that exceed a situation score threshold areeligible to be conveyed to AWM 2310. Such concepts can be conveyed whenthere is sufficient available storage in AWM 2310 to retain the conceptand there are no disparate concepts with a higher situation score thathave not been conveyed to AWM 2310. A concept's suitability, and thus aconcept's situation score, in AWM 2310 can decay as time progresses,which can allow new concepts with a higher suitability to enterawareness working memory 2310 when one or more concepts already inmemory are no longer needed or are no longer applicable. It is notedthat the larger the concept's inertia the longer it takes the concept toboth be conveyed to and be removed from AWM 2310.

When a manufacturing platform/tool system state changes, e.g., a sputtertarget is replaced, an electron beam gun is added, a deposition processis finished, an in situ probe is initiated, an annealing stage iscompleted, and so on, awareness planner 2350 überbots can document whichconcepts (e.g., knowledge elements) can be applied in the new state, andcan increase a suitability value, and thus a situation score, of eachsuch concept in AKM 2330. Similarly, the activation energy of autobots2215 ₁-2215 _(N) can be adjusted by uberbots in order to reduce theactivation energy of specific autobots, and to increase E_(A) forautobots that are appropriate to a new situation. The increment insuitability (and situation score) can be spread by planner überbots tothose concepts' first neighbors and then to second neighbors, and soforth. It should be appreciated that a neighbor of a first concept inAKM 2330 can be a second concept that resides, in a topological sense,within a specific distance from the first concept according to aselected measure (e.g., number of hops, Euclidean distance, etc.) It isnoted that the more distant a second concept is from a first conceptthat received an original increment in suitability, the smaller thesecond concept's increment in suitability. Thus, suitability (andsituation score) increments present a dampened spread as a function of“conceptual distance.”

In architecture 2100, self-awareness component 2150 comprises anawareness schedule adapter (ASA) 2360 which can be an extension ofawareness planner component 2350 and can request and effect changes incollection extrinsic data or intrinsic data (e.g., via sensor component1925 through interaction component 1930, via input 1730, or via(feedback) link 1755). In an aspect, awareness schedule adapter 2360 canintroduce data sampling frequency adjustments—e.g., it can regulate arate at which different adaptors in adaptor component 1935 can conveydata to knowledge network 1975 (e.g., information input 1958) intendedfor ASM 2320. Moreover, awareness schedule adapter 2360 can sample atlow frequency, or substantially eliminate, collection of data associatedwith process variables that are not involved in the description ofnormal patterns of data, or variables that fail to advance theaccomplishment of a goal as inferred from data received in an adaptiveinference engine 1710. Conversely, ASA 2360 can sample at higherfrequency a set of variables extensively used in a normal pattern ofdata, or that can actively advance a goal. Furthermore, when theautonomous learning system 1960 acknowledges a change of state ofmanufacturing platform/tool system 1910 (or a change in a situationassociated with a specific goal) wherein measured data indicates thatproduct quality or process reliability are gradually deviating fromnormal data patterns (or a goal drift is resulting in significantdeparture from an initial goal in the space of goals or thatnon-conformities exist), the autonomous learning system can request, viaASA 2360, a more rapid sampling of data to collect a larger volume ofactionable information (e.g., input 1730) that can effectively validatethe non-conformities and process degradation and trigger an appropriatecorrective processing action or active interdiction.

An actor 1990 (e.g., a human agent) can train self-awareness component2150 in multiple manners, which can include a definition of one or moreepisodes (including, for instance, illustrations of successfully adaptedgoals). A training of the autonomous learning system 1960, throughself-awareness component 2150, for an episode can occur as follows. Theactor 1990 creates an episode and provides the episode with a uniquename. Data for the newly created episode can then be given to autonomouslearning system 1960. The data can be data for a specific sensor duringa single specific operation step of a tool system 1910, a set ofparameters during a single specific step, a single parameter average fora run, etc.

Alternatively, or additionally, more elementary guidance can be providedby actor 1990. For example, a field support engineer can performpreventive tool maintenance (PM) on tool system 1910. PM can be plannedand take place periodically, or it can be unplanned, or asynchronous. Itshould be appreciated that preventive tool maintenance can be performedon the manufacturing system in response to a request by the autonomouslearning system 1960, in response to routine preventive maintenance, orin response to unscheduled maintenance. A time interval elapses betweenconsecutive PMs, during such a time interval one or more processes(e.g., wafers/lots manufacturing) can take place in the tool system.Through data and product assets and associated information, such aseffected planner and unplanned maintenance, autonomous learning systemcan infer a “failure cycle.” Thus, the autonomous learning system canexploit asset(s) 1928 to infer a mean time between failures (MTBF). Suchinference is supported through a model of time-to-failure as a functionof critical data and product assets. Furthermore, autonomous learningsystem 1960 can develop models, through relationships among disparateassets received as information I/O 1958 or through historic dataresulting from supervised training sessions delivered by an expertactor. It should be appreciated that an expert actor can be a disparateactor that interacts with a trained disparate autonomous learningsystem.

Actor 1990 can guide the autonomous system by informing the system thatit can average wafer level run data and assess a drift in criticalparameters across PM intervals. A more challenging exercise can also beperformed by the autonomous system, wherein the actor 1990 indicatesthrough a learning instruction to autonomous learning system 1960 tolearn to characterize a pattern of data at the wafer average levelbefore each unplanned PM. Such an instruction can promote the autonomouslearning system 1960 to learn a pattern of data prior to an unplannedPM, and if a pattern of data can be identified by an awareness autobot,the self-awareness component 2150 can learn such a pattern as timeevolves. During learning a pattern, awareness component 2150 can requestassistance (or services) from self-conceptualization component 2160 orawareness autobots that reside in autobot component 2140. When a patternfor the tool system is learned with a high degree of confidence (e.g.measured by a degree of reproducibility of the pattern as reflected incoefficients of a PCA decomposition, a size of a dominant cluster in aK-cluster algorithm, or a prediction of the magnitude of a firstparameter as a function of a set of disparate parameters and time, andso forth), autonomous biologically based learning system 1960 can createa reference episode associated with the malfunction that can lead to theneed of tool maintenance so that an alarm can be triggered prior tooccurrence of the reference episode. It is noted that awarenessautobots, which can reside in autobot component 2140, can fail tocharacterize completely a data pattern for the malfunction referenceepisode, or substantially any specific situation that can requireunplanned maintenance, before it is necessary. It should be appreciatednonetheless that such a preventive health management of a tool system1910, which can include a deep behavioral and predictive functionalanalysis, can be performed by autobots in self-conceptualizationcomponent 2160.

FIG. 24 is a diagram 2400 of autobots that can operate in an awarenessworking memory 2320. Illustrated autobots—quantifier 2415, expectationengine 2425, surprise score generator 2435, and summary generator2445—can compose an awareness engine; a virtual emergent component,whose emergent nature arises from the concerted operation of elementaryconstituents, e.g., autobots 2415, 2425, 2435, and 2445. It should beappreciated that the awareness engine is an example of how one or moreplanning überbots can use a collection of coordinated autobots toperform a sophisticated activity. The planning überbots employ thevarious autobots (e.g., average, standard deviation, PCA, wavelet,derivative, etc.) or the services of self-conceptualization component1560 to characterize a pattern of the data received in an autonomousbiologically based learning system. Data for each step, run, lot, etc.run can be labeled by an external entity as being normal or abnormalduring training. Quantifier 2415 can be employed by planning überbots toexploit normal data to learn a pattern of data for a prototypical,normal process. In addition, quantifier 2415 can assess an unlabeleddata set (e.g., information input 1958) that is deposited into ASM 2320and compare the normal data pattern with a data pattern of unlabeleddata. Expected patterns for normal data or equations to predictparameters with normal data can be stored and manipulated throughexpectation engine 2425. It should be noted that the pattern ofunlabeled data can differ from the normal data pattern in various ways,according to multiple metrics; for instance, a threshold for a HotellingT2 statistic (as applied to PCA and MS-PCA and derived from trainingruns) can be exceeded; an average of a data subset of the unlabeled dataset can differ by more than 36 (or other predetermined deviationinterval) from the average computed with normal, training run data; adrift of measured parameters can be substantially different from thatobserved in the data associated with a normal run; and so forth. Summarygenerator 2445 thus generates a vector of components for normal data,whereas surprise score generator 1835 can incorporate, and rank orweight substantially all such differences in components of the vectorand compute a net degradation surprise score for the tool system thatreflect a health condition of the tool system and reflect how far “awayfrom normal” the tool system is. It should be appreciated thatdiscrepancies among a normal and unlabeled metric can vary as a functionof time. Thus, through collection of an increasing amount of normaldata, the autonomous learning system 1960 can learn various operationallimits with greater level of statistical confidence as time evolves andcan adjust manufacturing process recipes (e.g., a goal) accordinglydegradation condition, as measured through a surprise score, forexample, can be reported to an actor via summary generator 2445.

FIG. 25 illustrates and example embodiment 2500 of aself-conceptualization component of an autonomous biologically basedlearning system. A functionality of self-conceptualization component isto build an understanding of important semiconductor manufacturing toolrelationships and descriptions. Such an understanding can be employed toadjust a manufacturing process (e.g., a goal). This acquiredunderstanding is built autonomously or in conjunction with end-user(e.g., actor 1990) supplied guidance. Similarly, to the other primaryfunctional components 2150 and 2160, self-conceptualization component2160 is assembled or defined recursively in terms of a hierarchy ofmemories, operational units, or autobots, and planners; such componentscan communicate a priority-enabled knowledge network.

Embodiment 2500 illustrates a conceptualization knowledge memory (CKM)2510 that includes concepts (e.g., attributes, entities, relationships,and procedures) necessary for operation of self-conceptualizationcomponent 2160. Concepts in CKM 2510 include (i) domain specificconcepts such as a step, a run, a lot, a maintenance-interval, awet-clean-cycle, a step-measurements, a wafer-measurements, alot-measurements, a location-on-wafer, a wafer-region, a wafer-center, awafer-edge, a first-wafer, a last-wafer, etc.; and (ii) general purpose,domain independent concepts like a number, a constant (e.g., e, π), avariable, a sequence, a time-sequence, a matrix, a time-matrix, afine-grained-behavior, a coarse-grained-behavior, etc.Self-conceptualization component also includes a vast array of generalpurpose functional relations such as add, subtract, multiply, divide,square, cube, power, exponential, log, sine, cosine, tangent, of and soforth, as well as other domain specific functional relations that canpresent various levels of detail and reside in adaptiveconceptualization template memory (ACTM) 2520.

ACTM 2520 is an extension of CKM 2510 that can hold functionalrelationships that are either completely or partially known to an actor(e.g., an end user) that interacts with a tool system 1910 (asemiconductor manufacturing platform tool). It should be noted thatwhile ACTM is a logical extension of CKM, autobots, planners, and otherfunctional components are not affected by such separation, as the actualmemory storage can appear a single storage unit withinself-conceptualization component 2160. Self-conceptualization component2160 can also include a conceptualization goal memory (CGM) 2530 whichis an extension of a conceptualization working memory (CWM) 2540. CGM2530 can facilitate autobots of a current goal, e.g., to learn (f,pressure, time, step); for a particular process step, learn a function fof pressure wherein the function depends on time. It should be notedthat learning function f represents a sub-goal that can facilitateaccomplishing the goal of manufacturing a semiconductor device utilizingtool system 1910.

Concepts in ACTM 2520 also have a suitability numeric attribute and aninertia numeric attribute, which can lead to a situation score. A valueof inertia can indicate a likelihood of a concept to be learnt. Forexample, a higher inertia value for a matrix concept and a lower inertiafor a time-sequence concept can lead to a situation whereself-conceptualization component 2160 can learn a functional behavior oftime-sequences rather than a functional behavior of data in a matrix.Similarly, to self-awareness component 2150, concepts with lower inertiaare more likely to be conveyed from CKM 2510 to CWM 2540.

Conceptual planners (CPs) provide activation energy to the variousautobots and provide situation energy to various concepts in CKM 2510and ACTM 2520, as a function of a current context, a current state oftool system 1910 (or generally a goal component 1720), a content of CWM2540, or current autobot(s) active in CWM 2540. It should be appreciatedthat activation energy and situation energy alterations can lead to goaladaptation based on the knowledge generated (e.g., based on learning) asa result of the altered semantic network for concepts in CWM 2540 or CKM2510—as inference by an adaptive inference engine can be based onpropagation aspects of concepts.

Contents of CTM 2520 are concepts which can describe the knowledgediscussed above, and thus those concepts can have suitability andinertia numeric attributes. The contents of CTM 2520 can be used byautobots to learn the functional behavior of the tool system 1910(subject to the constraint that concepts with lower inertia are morelikely to be activated over concepts with higher inertia). It is notnecessary for all guidance to have the same inertia; for instance, afirst complete function can be provided a lower inertia than a secondcomplete function even though both concepts represent completefunctions.

When partial knowledge like a partially-defined equation is uploaded inCWM 2540, it can be completed, e.g., with existing knowledge—CPscoordinate autobots to employ available data to first identify valuesfor unknown coefficients. A set of ad hoc coefficients can thus completethe partially-defined equation concept into a complete function concept.The complete equation concept can then be utilized in a pre-builtfunctional-relation concept such as add, multiply, etc. Basic knowledgewith output (e.g., relationship(output(K_(E)),T)) can facilitateautobots in CWM 2540 to construct and evaluate various functionaldescriptions that involve data for K_(E) and T in order to identify thebest function that can describe a relationship among K_(E) and T.Alternatively, basic knowledge without output can facilitate autobots,with assistance of CPs, to specify a variable as an output, orindependent, variable and attempt to express it as a function of theremaining variables. When a good functional description is not found, analternative variable can be specified as an independent variable theprocess is iterated until it converges to an adequate functionalrelationship or autonomous learning system 1960 indicates, for exampleto actor 1990, that an adequate functional relationship is not found. Anidentified good functional relationship can be submitted to CKM 2510 tobe utilized by autobots in autonomous learning system 1960 with a levelof inertia that is assigned by the CPs. For instance, the assignedinertia can be a function of the mathematical complexity of theidentified relationship—a linear relationship among two variables can beassigned an inertia value that is lower than the assigned inertia to anon-linear relationship that involve multiple variables, parameters, andoperators (e.g., a gradient, a Laplacian, a partial derivative, and soon).

Conceptualization engine 2545 can be a “virtual component” that canpresent coordinated activities of awareness autobots andconceptualization autobots. In an aspect, self-awareness component 2150can feed forward (through FF loop 2152) a group of variables (e.g.,variables in the group can be those that display good pairwisecorrelation properties) to self-conceptualization component 2160.Forwarded information can facilitate self-conceptualization component2160 to check CKM 2510 and ACTM 2520 for function relation templates.The availability of a template can allow an autobot of aconceptualization learner (CL), which can reside in theconceptualization engine 2545, to more quickly learn a functionalbehavior among variables in a forwarded group. It should be appreciatedthat learning such a functional behavior can be a sub-goal of a primarygoal. A CL autobot with the assistance of a CP autobot can also useautobots of a conceptualization validator (CV). CV autobots can evaluatea quality of proposed functional relationships (e.g., average errorbetween a predicted value and a measurement is within instrumentresolution). A CL autobot can independently learn a functionalrelationship either autonomously or through actor-supplied guidance;such actor supplied guidance can be regarded as extrinsic data.Functions learned by a CL can be fed back (e.g., via FB link 2158) toself-awareness component 2150 as a group of variables of interest. Forexample, after learning the function k_(E)=k₀ exp(−U/T), wherein k₀(e.g., an asymptotic etch rate) and U (e.g., an activation barrier)possess specific values known to the CL, self-conceptualizationcomponent 2160 can feed back the guidance group (output(K_(E),T) toself-awareness component 2150. Such feedback communication can affordself-awareness component 2150 to learn patterns about such group ofvariables so that degradation with respect to the group of variables canbe quickly recognized and, if necessary, an alarm generated (e.g., analarm summary, an alarm recipient list verified) and triggered. Memory2560 is a conceptualization episodic memory.

The following two aspects related to CL and CV should be noted. First,CL can include autobots that can simplify equations (e.g., throughsymbolic manipulation), which can facilitate to store a functionalrelationships as a succinct mathematical expression. As an example, therelationship P=((2+3)ϕ)((1+0)÷θ) is simplified to P=3ϕ÷θ, where P, ϕ andθ indicate, respectively, a pressure, a flow and an exhaust valve angle.Second, CV can factor in the complexity of the structure of an equationwhen it determines a quality of the functional relationship—e.g., forparameters with substantially the same characteristics, like averageerror of predicted values versus measurements, a simpler equation can bepreferred instead of a more complicated equation (e.g., simpler equationcan have lower concept inertia).

Additionally, important FF 2152 communication of information fromself-awareness component 2150 to self-conceptualization component 2160,and FB 2158 communication from self-conceptualization component 2160 toself-awareness component 2150, can involve cooperation of awarenessautobots and conceptualization autobots to characterize a pattern ofdata for an episode. As discussed above in connection with FIG. 21, whenself-awareness component 2150 fails to learn an episode,self-conceptualization component 2160 can assist self-awarenesscomponent 2150 through provision of a set of relevant functionalrelationships. For example, characterization of an episode can require afine-grained description of time dependence of a pressure in astabilization step in a process run in a tool system 1910.Self-conceptualization component 2160 can construct such a detailed(e.g., second by second) time dependence of the pressure in thestabilization step. Thus, through FB loop 2158, self-awareness component2150 can learn to characterize the pattern of pressure during thestabilization step in a normal tool situation and to compare the learntpressure time dependence with a pattern of pressure in a specificepisode data. As an illustration, presence of a spike in a measuredpressure prior to a stabilization step for data in an episode, and theabsence of the spike in pressure data during normal tool operation canbe detected as a data pattern that identifies the occurrence of theepisode in an autonomous biologically based learning tool 1900.

Similarly, a prediction of an unscheduled PM can rely on knowledge oftemporal fluctuations of critical measurements of tool system data andthe availability of a set of predictive functions conveyed byself-conceptualization component 2170. The predictive functions canassist a self-awareness component (e.g., component 2150) to predict anemerging situation of an unplanned PM in cases where the predictiondepends on projected values of a set of variables as a function of time.

FIG. 26 illustrates an example embodiment 2600 of a self-optimizationcomponent in an autonomous biologically based learning system. Asindicated above, self-optimization component functionality is to analyzethe current health (e.g., performance) of a manufacturing platform/toolsystem 1910 and then determine if non-conformities are detected and,based on the results of the current health analysis, diagnose or ranksubstantially all potential causes for health deterioration of the toolsystem 1910 and the cause of such non-conformities, and identify a rootcause of non-conformities based on learning acquired by autonomouslearning system 1960 in order to provide the necessary control of themanufacturing platform to provide corrective processing. Analogously tothe other primary functional components 2150 and 2160, self-optimizationcomponent 2170 is built recursively from a hierarchy of memories thatcan belong to a memory platform 1965, and autobots and planners whichcan be a part of a processing platform 1985.

Optimization knowledge memory (OKM) 2610 contains concepts (e.g.,knowledge) related to diagnosis and optimization of the behavior ofmanufacturing platform/tool system 1910. It should be appreciated that abehavior can include a goal or a sub-goal. Accordingly, OKM 2610contains domain, or goal, specific concepts such as step, step-data,run, run-data, lot, lot-data, PM-time-interval, wet-clean-cycle,process-recipe, sensor, controller, etc. The latter concepts areassociated with a tool system 1910 that manufactures semiconductordevices. In addition, OKM 2610 comprises domain independent concepts,which can include a measurement (e.g., measurements from measurementmodules), a sequence, a comparator, a case, a case-index, acase-parameter, a cause, an influence, a causal-dependency, an evidence,a causal-graph, etc. Furthermore, OKM 2610 can comprise a set offunctional relations like compare, propagate, rank, solve, etc. Suchfunctional relations can be exploited by autobots, which can reside inautobot component 2140 and can confer OKM 2610 at least a portion of itsfunctionality through execution of procedures. Concepts stored in OKM2610 possess a suitability numeric attribute and an inertia numericattribute, and a situation score attribute derived there from. Thesemantics of suitability, inertia and situation score is substantiallythe same as that for self-awareness component 2150 andself-conceptualization component 2160. Therefore, if a run-data isprovided with a lower inertia than step-data, self-optimizationcomponent 2170 planners (e.g., überbots) are more likely to communicatethe concept of run-data from OMK 2610 to optimizing working memory (OWM)2620. In turn, such inertia relationship between run-data and step-datacan increase the activation rate of optimization autobots that work withrun related concepts.

It should be noted that through FF links 2152 and 2162, self-awarenesscomponent 2150 and self-conceptualization component 2160 can influencethe situation score of concepts stored on OKM 2610, and the activationenergy of optimization autobots through optimization planners (OPs),which can reside in optimization planner component 2650. It should beappreciated that concepts which are stored in OKM 2610 and areinfluenced through self-awareness component 2150 andself-conceptualization component 2160, can determine aspects of aspecific goal to be optimized as a function of a specific context. As anillustration, if self-awareness component 2150 recognizes that a patternof data for a process step has degraded significantly and producednon-conformities in a workpiece, the situation score of the associatedstep concept can be increased. Accordingly, OPs can then supplyadditional activation energy to optimizing autobots related to the stepconcept in order to modify a set of steps executed during a process toprovide corrective processing (e.g., while pursuing a goal). Similarly,if self-conceptualization component 2160 identifies a new functionalrelationship among tool measurements for a product lot, FF informationreceived from self-conceptualization component 2160 (via FF 2162, forexample) self-optimization component 2170 can increase (1) a situationscore of a lot concept and (2) an activation energy of an optimizationautobot with a functionality that relies on a lot concept; therefore,modifying aspects of the lot concept (e.g., number or type of wafers ina lot, cost of a lot, resources utilized in a lot, and so on).

Health assessment of a tool system 1910 can be performed throughdiagnosing engine 2425 as discussed. It should be noted that a healthassessment can be a sub-goal of a manufacturing process. Diagnosingengine 2425 autonomously creates a dependency graph and allows actor1990 to augment the dependency graph. (Such a dependency graph can beregarded as extrinsic data or as intrinsic data.) The causal graph canbe conveyed incrementally, according to the dynamics of the processconducted by the tool system 1910, and a diagnosis plan that can bedevised by the actor 1990. For example, a causal graph can show that a“pressure” malfunction is caused by one of four causes: a depositionchamber has a leak, gas flow into the chamber is faulty, exhaust valveangle (which controls the magnitude of gas flow) is faulty, or apressure sensor is in error. Components of tool system 1910 have apriori probabilities of failure (e.g., a chamber leak can occur withprobability 0.01, a gas flow can be faulty with probability 0.005, andso on). In addition, actor 1990, or self-conceptualization component2160, can define a conditional dependency for pressure malfunction whichcan be expressed as a conditional probability; e.g., probability ofpressure being at fault given that the chamber has a leak can bep(P|leak). Generally, conditional probabilities causally relatingsources of tool failure can be provided by actor 1990. It should benoted that autonomous learning system 1960 assumes that probabilityassignments defined by actor 1990 can be approximate estimates, which inmany cases can be significantly different from a physical probability(e.g., actual probability supported by observations). Examples of causalgraphs are presented and discussed next in connection with FIGS. 27A and27B below.

Self-optimization component 2170 can also comprise a prognosticcomponent 2660 which can generate a set of prognostics regardingperformance of manufacturing platform/tool system 1910 throughinformation I/O 1958 associated with the tool. Such information cancomprise quality of materials employed by functional component, physicalproperties of product assets 1928 produced by manufacturingplatform/tool system 1910, such as index of refraction, opticalabsorption coefficient, or magnetotransport properties in cases productassets 1928 are doped with carriers, etc. Multiple techniques can beutilized by prognostics component 2660. The techniques comprise firstcharacterization techniques substantially the same as those techniquesthat can be employed by self-awareness component when processinginformation 1958; namely, such as (i) frequency analysis utilizingFourier transforms, Gabor transforms, wavelet decomposition, non-linearfiltering based statistical techniques, spectral correlations; (ii)temporal analysis utilizing time dependent spectral properties (whichcan be measured by sensor component 1925), non-linear signal processingtechniques such as Poincaré maps and Lyapunov spectrum techniques; (iii)real- or signal-space vector amplitude and angular fluctuation analysis;(iv) anomaly prediction techniques and so forth. Information, or dataassets generated through analysis (i), (ii), (iii) or (iv) can besupplemented with predictive techniques such as neural-networkinference, fuzzy logic, Bayes network propagation, evolutionaryalgorithms, like genetic algorithm, data fusion techniques, and so on.The combination of analytic and predictive techniques can be exploitedto facilitate optimization of tool system 1910 via identification ofailing trends in specific assets, or properties, as probed by sensorcomponent 1925, as well as information available in OKM 2610, withsuitable corrective measures generated by optimization planner component2650, and optimization autobots that can reside in component 2140.

FIG. 27A illustrates an example causal graph 2700 generated byself-conceptualization component 2130. A causal graph represents arelationship between dependent and independent variables of mathematicalfunction, or relationship, predicted by self-conceptualization component2130. As an example, by accessing data for pressure (P), gas flow (ϕ),and valve angle (κ), self-conceptualization component 2130 can use oneor more mathematical techniques, such as curve fitting, linearregression, genetic algorithm, etc. to conceptualize, or learn, apredictive function 2710 for an output of interest or dependentvariable, e.g., pressure, as a function of data inputs or independentvariables—gas flow, valve angle, temperature, humidity, etc. An examplelearnt predictive function 2710 can be the following relationshipbetween pressure and the two input variables ϕ, θ: P=2π(ϕ/θ³). From sucha learnt function, self-conceptualization component 2160 autonomouslyconstructs the dependency graph 2700.

To generate the dependency graph 2700 self-conceptualization component2160 can proceed in two steps. (i) Comparator 2720 is introduced as aroot node that receives as input a single learnt function 2710. Afailure in comparator 2720 implies a failure in manufacturingplatform/tool system 1910 that employs a biologically based autonomouslearning system. A comparator failure can be a Boolean value (e.g.,“PASS/FAIL” 2730) result which can be based on comparing a measuredvalue for example, of a workpiece attribute with a predicted valuegenerated through learnt function 2710. Self-conceptualization component2160 flags a failure in comparator 2720 when the average differencebetween predicted pressure values and collected pressure data (e.g., asreported by a pressure sensor residing in sensor component) fails toremain within user-specified bounds—e.g., average difference is toremain within 5% of predicted pressure. A failure of comparator 2720 ismade dependent on the output of the predictive function 2710. Thus, acomparator failure depends on (is influenced by) the failure of thepressure reading (P_(R) 2740); which can fail because a pressure sensor(P_(S) 2743) has failed or a physical pressure (e.g., the physicalquantity P_(P) 2746) has failed. Physical pressure P_(P) 2746 can failbecause a pressure mechanism (P_(M) 2749) can fail. Thus, the systemautonomously creates the dependencies between P_(R) 2740 and {P_(S)2743, P_(P) 2746} and between P_(P) 2740 and {P_(M) 2749}.

(ii) Dependent variables in learnt function 2710 are employed tocomplete the dependency graph as follows. Physical mechanism P_(M) 2749can fail when a gas-flow reading (ϕ_(R) 2750) fails or a valve-anglereading (θ_(R) 2760) fails—dependent variables in learnt function 2710.Thus, self-conceptualization component 2160 creates dependencies betweenP_(M) 2749 and {θ_(R) 11150, ϕ_(R) 2760}. Substantially the sameprocessing, or reasoning, for a failure in a reading can be employed byself-conceptualization component 2160 to create dependencies betweenϕ_(R) 2750 and {ϕ_(S) 2753, ϕ_(P) 2756} and between θ_(R) 2760 and{θ_(S) 2763, θ_(P) 2766}. Self-conceptualization component 2160 then canadd the dependency between ϕ_(R) 2756 and {_(M) 2759} and between θ_(P)and {θ_(M)}. It is to be noted that the relationship between thephysical quantity (e.g., P_(P) 2746, ϕ_(P) 2756, θ_(P) 2766) and theassociated mechanism (e.g., P_(M) 2749, ϕ_(M) 2759, and θ_(M) 2769) isredundant and presented to enhance clarity—mechanism nodes (e.g., nodes2749, 2759, and 2769) can be removed, and their children made thechildren of the associated physical magnitude nodes (e.g., nodes 2746,2756, and 2769).

In a dependency graph such as dependency graph 2700, leaf-level nodesare physical points of failure; e.g., nodes 2740, 2743, 2746, and 2749;nodes 2740, 2753, 2756, and 2759; and 2760, 2763, 2766, and 2769. In anaspect, an actor (e.g., actor 1990, which can be a user) can supply abiologically autonomous learning system with a priori probabilities forall physical points of failure. Such a priori probabilities can beobtained from manufacturing specifications for the component, fielddata, MTBF data, etc., or can be generated by simulation of theperformance of parts present in a manufacturing tool and involved in arelevant manufacturing processing. The actor can also supply conditionalprobabilities based on prior experience, judgment, field data, andpossible failure modes (e.g., the presence of a first failure caneliminate the possibility of a second failure, or the first failure canincrease the probability of occurrence of the second failure, etc.).Upon receiving a priori and conditional probabilities, for example viaan interaction component, such as component 1940, the autonomous systemcan use Bayesian network propagation with learning to update theprobabilities based on actual failure data submitted to the autonomoussystem. Thus, in case the initial probabilities provided by the actorare erroneous, the autonomous system adjusts the probabilities as fielddata contradicts or supports a failure outcome; namely, a PASS or FAILresult of a comparator.

It should be noted that an actor (e.g., actor 1990, which can be a user)can add dependencies to an autonomously generated dependency graph(e.g., dependency graph) rooted at mechanism failures. Such an additioncan be effected, for instance, through interaction manager 1955. In anaspect, as an illustration, dependency graph 2700 is augmented with twonodes labeled P_(LEAK) 2770 and P_(ALT) 2773 that result in a dependencyof P_(M) 2749 on {ϕ_(R) 2750, θ_(R) 2760, P_(LEAK) 2770, and P_(ALT)2773}. It is to be appreciated that dependency graph 2700 can beaugmented with a deeper graph as well. Addition of node P_(LEAK) 2770informs the autonomous system, through self-conceptualization component2160, that besides a failure of a gas flow reading or a valve anglereading, the pressure mechanism can also fail should a leak be presentin the tool. Node P_(ALT) 2773 is complementary to node 2770 in that itrepresents the likelihood that mechanisms alternative to a leak resultsin system failure. Upon addition of a node, or a deeper graph, the actoris to assign a priori probabilities for the node and associatedconditional probabilities describing the dependencies.

It should be appreciated that learnt functions can be more complex thanthe function P=F(ϕ,θ) discussed above, and can include substantiallymore independent variables; however, causal graphs can be prepared insubstantially the same manner.

FIG. 27B is a diagram 2780 of an example learnt function dependencygraph with prediction and recipe comparators. In addition tolearnt-function comparators (e.g., comparator 2720), a biologicallybased autonomous learning system can generate one or more recipecomparators. A recipe comparator (e.g., comparator A 2795 _(A) orcomparator B 2795 _(B)) compares a set value of a recipe parameter witha corresponding average measure value, or reading, that arises from anassociated sensor in a tool system (e.g., tool system 1910). In anaspect, given a collection of recipe parameters (e.g., θ 2785 _(A) or ϕ2785 _(B)) that have an associated sensor and corresponding prescribedvalues, the autonomous system generates a recipe comparator for each setparameter. Similarly, to a predicted function comparator, if the setrecipe value and the reading differ by a specific threshold which can bedetermined by an actor (e.g., actor 1990), the recipe comparator signalsfailure. It should be noted that in diagram 2780 a recipe comparator forpressure is not generated since a process pressure is not set to aspecific value.

In order to identify a root cause, e.g., the physical point of failurewith the highest probability of failure, a biologically based autonomouslearning system can utilize a failure of one or more predictor or recipecomparators to rank all physical points of failure present in adependency graph. In an aspect, for a complete dependency graph with oneor more comparators, the biologically based autonomous learning systemcan use Bayesian inference to propagate the probabilities given thefailure signature of the comparators. Thus, the system can compute theprobability of failure for a particular PASS/FAIL outcome (e.g., outcome2798 _(A) for comparator A 2795 _(A) or outcome 2798 _(B) for comparatorB 2795 _(B)) for each comparator. As an example, suppose that predictorcomparator 2720 and recipe comparator A 2795 _(A) fail whereascomparator B 2795 _(E) passes. The autonomous system can compute thefailure probability for each physical point of failure given thecomparator failures. (For example, what is the probability of thepressure sensor failure given that comparator 2795 _(A) and comparator A2795 _(A) fail whereas comparator B 2795 _(E) passes). Each point offailure is then ordered from most likely to fail (highest computedprobability), or the most likely root cause, to least likely to fail(lowest computed probability). Identification of a root cause, which canbe deemed as actionable intelligence (e.g., output 1740), can beconveyed to an actor via an interaction manager for further process;e.g., order a new part, request a maintenance service (an actorcommunicates with or resides in the tool's manufacturer location),download a software update, schedule a new training session, and thelike.

FIG. 28 illustrates a high level block diagram 2800 of an example groupdeployment of autonomous biologically based learning tool systems. Thegroup of autonomous tools systems 2820 ₁-2820 _(K) can be controlled byan autonomous biologically based learning tool 1960 which receives(input) and conveys (output) information 1958 to an interface 1930 thatfacilitates an actor 1990 to interact with the group of autonomous toolssystem 2820 ₁-2820 _(K) and with autonomous learning system 1960.Individually, each of the autonomous tool systems 2820 ₁-2820 _(K) aresupported, or assisted, by associated autonomous learning systems 2850.Such learning system possesses substantially the same functionality oflearning system 1960. It should be appreciated that in group 2810 eachof autonomous tools 2820 ₁-2820 _(K) can afford independent interaction,respectively, with associated local actors 1990 ₁-1990 _(K). Such actorpossesses substantially the same functionality as actor 1990, asdiscussed in connection with FIG. 19 above. Additionally, an interactionwith autonomous tools 2820 ₁-2820 _(K) takes place in substantially thesame manner as in autonomous system 1900, through an interactioncomponent 2840 and by providing and receiving tool-specific information(e.g., 2848 ₁-2848 _(K)) and assets, which both are typically toolssystem specific (e.g., assets 2850 ₁-2850 _(K)). In particular, itshould be appreciated that in group deployment 2812, each of actors 1990₁-1990 _(K) can monitor disparate aspects of operation its associatedsystem tool (e.g., system tool 2820 ₂). As an example, local actors 1990₁-1990 _(K) can establish a set of specific outputs (e.g., 2860 ₁-2860_(K)) to be critical. Such a determination can be based on historic dataor design (e.g., recipe for a process), or it can originate autonomouslythrough generated patterns, structures, relationships and the like. Inabsence of such a determination, group autonomous learning system 1960assumes substantially all outputs (e.g., 2860 ₁-2860 _(K)) leading togroup output 2865 are critical.

In an aspect, autonomous learning system 1960 can learn (throughlearning mechanisms described above in connection with system) expectedvalues for the critical output parameters during normal (e.g.,non-faulty) group tool 2800 operation. In an aspect, when measuredoutput 2865 deviates from an expected output, autonomous learning system1960 can identify a performance metric of group 2800 performance asdegraded. It should be appreciated that the latter assessment canproceed in substantially the same manner as described in connection withsingle autonomous tool system 1900; namely, through a self-awarenesscomponent in autonomous learning system 1390. It is to be noted thateven though autonomous group tool 2800 can present a degradedperformance, a subset of autonomous tool system 2801-2820K can provideoutput that is not degraded and meet individual expectation values for apredetermined metric.

In addition, similarly to the scenario of a single tool system (e.g.,tool system 1910), autonomous learning system 1960 can construct apredictive model for a critical output parameter as a function ofindividual tool related output parameters. It should be appreciated thatsuch output parameters can be collected through asset 1928 input/output.It is to be noted that in group tool 2800, measurements of tool output(e.g., 2860 ₁-2860 _(K)) can be available to autonomous biologicallybased learning system 1960 via sensor components residing in each oftool systems 2820 ₁-2820 _(K), which can be accessed through deployedknowledge network extant in each autonomous learning system (e.g., 1960,or 2850).

Furthermore, the autonomous system 1960 can also construct a predictivemodel of group time-to-failure as a function of assets 1928 of toolgroup or platform 2800; e.g., group input data, group outputs, grouprecipes, or group maintenance activities. In an aspect, to determine agroup time-to-failure, autonomous learning system 1960 can gatherfailure data, including time between detected (e.g., through a set ofsensor components or inspection systems) failures, associated assets2850 ₁-2850 _(K), outputs 2801-2860K, and maintenance activities forsubstantially all operation tools in the set of tools 2801-2820K. (Itshould be appreciated that as a consequence of prior failureassessments, specific tools (e.g., tool system 2 2820 ₁ and tool systemK 2820 _(K)) in the set of tools (e.g., tools 2820 ₁-2820 _(K)) in group2800 can be out of operation.) Collected data can be autonomouslyanalyzed (e.g., through a processing component 1985 in autonomouslearning system 1960) to learn a predictive function for time-to-failureas a function of the group assets (e.g., inputs, recipes, . . . ),outputs, and maintenance activities. It should be appreciated that thegroup time-to-failure model constructed from the collected data canreadily display substantially dominant factors that impact performanceof group tool 2800.

In an aspect, time-to-failure models constructed for individualcomponents of tool systems (e.g., 2820 ₁-2820 _(K)) in group tool 2800can be employed by actor 1990 (e.g., a group level controller) tooptimize part inventory and optimize maintenance scheduling. It shouldbe appreciated that such optimization can be conducted, at least inpart, by autonomous system 1960. For example, the autonomous systemaccesses the MES (or ERP) system to identify the number of availableparts. When a set of parts that provide functionality to tool systems2820 ₁-2820 _(K) (e.g., parts in one or more of components within afunctional component like a component 1915 in system 1910), and can beexpected to be necessary (e.g., for replacement) within a specific timeperiod Δ_(T), exceeds an available supply in stock, additional parts canbe ordered. Alternatively, or in addition, when parts are available, anexpected schedule of necessary parts can be analyzed to determine anoptimal, or adequate, time to place a new order.

It should be appreciated that maintenance schedules can be reassessedand optimized during a necessary, previously scheduled, maintenanceactivity, in order to exploit an opportunity available to autonomoussystem 1360 to analyze parts and identify parts that can fail in asubstantially short period of time. It should further be appreciatedthat a group or individual time-to-failure schedule can be complemented,autonomously in an aspect, with additional information such as cost ofparts, time to replace parts, and so forth, to determine whetherreplacement of a part during a current maintenance cycle is beneficialwith respect to the replacement of the part in a forthcoming scheduledmaintenance cycle. It is noted that autonomous system 1960 can also takeas input various costs associated with the operation of group tool 2800in order to compute a cost per output product (e.g., a workpiece, etc.)for the group, and a total cost to produce a specific order duringoperation of the group tool 2800. After building a model of cost as afunction of individual tool assets 2850 ₁-2850 _(K) (e.g., recipes),outputs 2860 ₁-2860 _(K), and maintenance activities, autonomous system1960 can rank individual tool systems 2820 ₁-2820 _(K) in increasingorder of operation cost. A combined cost data asset can be utilizedconstruct a predictive model of cost versus assets, outputs, andmaintenance activities associated with the individual tool systems—forexample, such an assessment can identify operational assets andvariables that affect substantially an operation or maintenance cost forthe group tool. In an aspect, autonomous system 1960 can utilizeavailable historic data assets to redesign a production line, orequipment configuration in a floor plant, in order to minimize costs. Inaddition, during such an optimization process, autonomous system 1960can rely on shutdown of various tool systems in order to exploitalternative patterns of operation. Furthermore, autonomous system 1960can utilize cost-benefit analysis to determine a set of trade-offscenarios in which production of specific output proceeds without outputfor specific, highly costly tool systems.

Tools system 2820 ₁-2820 _(K) can be substantially the same, or can bedisparate (e.g., tool systems 2820 ₁-2820 ₃ are steppers, tool 2820 _(j)is a stepper, and 2820K-2820K are turbomolecular vacuum pumps).Typically, a central difference amongst homogeneous (e.g., tool systemsare alike) and heterogeneous (e.g., tools are disparate) can lie in thatinput and output measurements (e.g., measurement assets) are distinct.For example, a critical output of interest for tool group or platform2800 can be D1 CD uniformity, but a coating or film-forming system thatis part of the group tool or platform 2800 can fail to provide suchoutput measurements. Accordingly, autonomous system 1960 can construct amodel for expressing a tool group's outputs as a function of individualtool (e.g., 2820 ₁-2820 _(K)) outputs. Thus, when a group performanceappears degraded, individual performances associated with individualtools can be analyzed to isolate a tool that has the largest weight incausing the performance degradation.

FIG. 29 illustrates a diagram of a conglomerate deployment of autonomoustool systems. Conglomerate system 2910 comprises a set of autonomoustool conglomerates 2920 ₁-2920 _(Q). Each of the tool conglomerates cancomprise homogeneous or heterogeneous groups of autonomous tools, e.g.,a set of disparate autonomous tools groups which can comprise anautonomous fabrication facility (not shown), or a set of disparateautonomous fabrication facilities. For example, the tool conglomeratesmay request manufacturing platforms. It should be appreciated thatautonomous conglomerates 2920 ₁-2920 _(Q) can typically be located indisparate geographic locations. Similarly, groups of autonomous tools ina factory can be deployed in disparate locations within a plant in viewthat a manufacturing process can comprise multiple steps. Accordingly,product output chain 2965 can facilitate providing disparate autonomoustool conglomerates 2920 ₁-2920 _(Q) with partially manufactured orprocessed or analyzed products; such features is indicated withbidirectional arrows 2960 ₁-2960 _(Q) which represent output/inputassociated with conglomerates 2920 ₁-2920 _(Q).

Conglomerate system 2910 can be autonomously supported by an autonomouslearning system comprising an interaction component 1940, an actor 1990,and an autonomous learning system 1960. In an aspect, autonomous supportcan be directed toward improving an overall fabrication effectiveness(OFE) metric of output assets (e.g., output 2965). In addition, each ofthe autonomous tool conglomerates 2920 ₁-2920 _(Q) can be in turnautonomously supported by an interaction component 2930, and anautonomous learning system 2940. Interface component 2930 facilitatesinteraction between autonomous learning system 2940 and actors 2990₁-2990 _(Q). Functionality of each of such components is substantiallythe same as the functionality of respective component described above inconnection with system 1960 and system 2800. Information 2948 _(I) (I=1,2, . . . , Q) communicated among interaction component 2930 andautonomous system 2940 is associated with the respective autonomous toolconglomerate 2920 _(I). Similarly, assets 2950 _(I) conveyed to andreceived from an autonomous tool conglomerate 2920 _(I) are specificthereof.

To address performance in an autonomous tool conglomerate 2910 ₁-2910_(Q), the multi-step characteristics of a fabrication process can beincorporated through a performance tag that identifies productsutilizing a composite conglomerate index C_(a), wherein the index aindicates a specific tool group within conglomerate C (e.g., autonomousconglomerate 2920 _(Q)), and a run index (R); thus, a product quality,or performance metric associated with a specific product is identifiedvia a label (C_(a);R), which can be termed “group-layer output.” Suchlabel facilitates identifying each autonomous operation group as anindividual component C_(a). Therefore, autonomous system 1960 can mapquality and performance metrics as a function of fabricationconglomerate (e.g., autonomous tool conglomerate 2910 ₂) and as afunction of tool group within each fabrication conglomerate. The latterfacilitates root-cause analysis of poor performance or quality, by firstidentifying a conglomerate (e.g., a fabrication facility) andsubsequently performing the analysis for the tool associated with theassessed degradation. It should be appreciated that index C_(a) toaccount for the fact that output assets generated in an autonomoussystem comprised of multiple conglomerate tools can be transported froma first conglomerate (N) to a second conglomerate (N′). Thus, thecomposite symbol for tracking performance associated with a transfer ofassets (e.g., as a part of a multi-step fabrication process) can readC_(a;N→N′).

Performance of an autonomous tool conglomerate can be performed as afunction of product yield. Such yield is utilized to rank disparateconglomerates. In an aspect, autonomous learning system 1960 can developa model for yield based at least in part on output assets from eachautonomous tool, or autonomous group tool. For example, for tools, orgroup of tools, employed in semiconductor manufacturing, yield can beexpressed as a function of detected non-conformities in workpieces basedon measured data. Moreover, other yield metrics can be utilized todetermine a model for a yield, especially in an autonomous learningsystems comprising tool conglomerates systems (e.g., 2920 ₁-2920 _(Q))wherein output assets can be transported among conglomerates: an overallequipment efficiency (OEE), a cycle time efficiency, an on-time-deliveryrate, a capacity utilization rate, a rework rate, a mechanical lineyield, a probe yield and final test yield, an asset production volume, astartup or ramp-up performance rate, etc. It is to be noted that anautonomous system that supports operation of a set of autonomous toolconglomerates can autonomously identify relationships amongst yieldmetrics in order to redesign processes or communicate with actors 1990₁-1990 _(Q) with respect to adjustments in connection to said yieldmetrics.

The yield function mentioned supra can be analyzed through a combinationof static and dynamic analysis (e.g., simulation) to rank group layeroutputs according to degree of influence, or weight, in leading to aspecific yield. It is to be noted that ranking tools, group of tools, orconglomerates, at a group-layer-output level based at least in part oninfluence in affecting asset output, or yield, can afford a group orconglomerate autonomous learning system 1960 to autonomously identify,through autonomous systems associated with each of the tools in a groupor group in a conglomerate, whether a specific tool can be isolated as adominant tool in yield deterioration. When such a tool is located, thegroup or conglomerate level autonomous system 1960 can issue an alarm toa maintenance department with information regarding ranking the faultsthat can be candidates for performance degradation.

In addition, yield for the lowest ranking autonomous tool conglomeratecan be employed to identify the group layer outputs of the tool groupthat is dominant in its impact on yield. The time-to-failure for suchtool-group can be compared with substantially the same tool groups indisparate autonomous conglomerates in order to identify cause(s) of poorperformance. Furthermore, an autonomous tool conglomerate system rankstools within a specific tool group in disparate tool conglomerates. Itis to be noted that an autonomous learning system that supports andanalyzes a group of autonomous tool conglomerates (e.g., 2920 ₁-2920_(Q)) can rank each of the conglomerates according to inferredtime-to-failure for each conglomerate. Since time-to-failure can changeover operational time intervals in view of, e.g., input/output asset(e.g., asset 1958) load, a database with time-to-failure projection canbe updated at specified periods of time (e.g., weekly, monthly,quarterly, or yearly).

Further yet, when an individual tool or module that is primarilyresponsible for a group tool's poor performance (e.g., the tool ranksthe lowest in performance within a group tool, such as a tool that mostfrequently fails to output assets with specified target properties ofquality like uniform doping concentration or uniform surface reflectioncoefficient) is identified, an autonomous system associated with thelowest performing tool, or with the conglomerate system that includessuch poor performing tool, can analyze the tool's outputs to identifythose outputs that most significantly affect the output of the lowestperforming group. For example, a tool in a tool group or conglomeratethat outputs assets with low uniformity as illustrates above, can leadto a substantial percentage (e.g., 60%) of tool groups uniformityvariation (for example, variation in uniformity change of surfacereflectivity of an optical display due to uniformity issues on surfacereflectivity of coatings on otherwise high-quality displays). To thatend, in an aspect, for each output in the group the tool autonomoussystem constructs a function that expresses tool output as a function oftool assets (e.g., inputs, recipes, and process parameters, tooloperator or actor, and so on). This model is then analyzed to identifythe dominant factors in poor performance. It is to be noted that anautonomous system can identify best performing tools in a group tool andanalyze causes that result in the tool having the best performance;e.g., the vacuum level of the tool during operation is consistentlylower than vacuum level of disparate tools in the group tool, or duringepitaxial deposition a wafer in the best performing tool rotates at alower speed than in disparate tool carrying out a deposition, thus thetool consistently achieves greater device quality. Such factors inhighest ranking and lowest ranking tools can be compared with sameparameters in other tools in conglomerate system. In case the comparisonindicates that the factors identified as the root causes of highest andlowest ranking performance appear to be substantially the samethroughout the tool conglomerate system, then a new model can bedeveloped, and alternative root causes can be identified. Suchiterative, autonomous processes of model development and validation cancontinue until root causes are identified and best practices areemulated (e.g., a coating recipe utilized in tool conglomerate 11320 pis adopted in substantially all tool conglomerates in view that itincreases output asset performance by a specific, desirable margin) androot causes for low performance are mitigated (e.g., abandoning aspecific brand of paint whose viscosity at the operating temperature ofa painting tunnel results in non-uniform coloration of paintedproducts). Ranking of tools, group of tools, or conglomerate of tools isautonomous and proceeds in substantially the same manner as in a singleautonomous tool system (e.g., system 1960). Autonomous systems thatsupport operation of a conglomerate of autonomous tools considers suchautonomous conglomerates as a single component regardless of thecomplexity of its internal structure, which can be accessed and managedthrough an autonomous system associated with the conglomerate.

FIG. 30 is a diagram 3000 that illustrates the modularity and recursivecoupling among classes of tools systems or manufacturing platforms orprocess modules described above—e.g., individual autonomous tool 1960,autonomous group tool 2800, and autonomous conglomerate tool 2900. Inautonomous system 3000, goals, contexts, and assets circulate throughknowledge network 1975 which is depicted as an axial gateway, and areconveyed to disparate autonomous tool systems 1960, 2800 and 2900. Suchinformation and assets are acted upon in each autonomous system, actscan include analysis, modification, generation of new information andassets; such acts are pictorially depicted as an arrow on the outer beltof each representation of autonomous systems 1960, 2800, 2900. Processedand generated assets are conveyed to the knowledge network 1975, wherecan be circulated among autonomous system. In diagram 3000, processingand generation of assets is represented as occurring azimuthally,whereas communication of assets is a radial process. As diagram 3000depicts, autonomous tool systems are based on substantially the sameelements that function in substantially the same manner.

FIG. 31 illustrates an example system 3100 that assesses, and reportson, a multi-station process for asset generation. An autonomous system3105 that comprises an autonomous biologically based learning system1960, an actor 1990, and associated interaction component 1930 canreceive and convey asset(s) 1928 that originate in an N-station process3110 and assess performance through backward chaining. The N-stationprocess is effected through a set of N process stations 3110 ₁-3110 _(N)that produce an output 3120 and can include individual autonomous tools1960, autonomous tool groups 2820, or autonomous tool conglomerates2920. As a result of performance assessment(s), autonomous system 3108can locate tools, or group of tools, in process stations 3110 ₁-3110_(N) with specific degrees of performance degradation. In addition, forthe selected station, autonomous system 3108 can provide an assessmentreport, a repair(s) report, or a maintenance schedule. It should beappreciated that disparate process stations can perform substantiallythe same operations; such a scenario would reflect the situation inwhich an output asset 3115 returns to a specific tool, or tool group,for further processing after the asset 3115 has been generated andtransported to a disparate tool, or group of tools, for furtherprocessing.

In backward chaining, action flow (e.g., process flow 3130) which leadsto an output typically counters a probe flow (e.g., assessment flow3140) which typically assesses the action flow. Thus, assessmentgenerally takes place in a top-bottom manner, in which assessment isconducted on a high-level stage of a specific action, e.g., a finalizedasset output 3120, and proceeds to lower-level stages in a quest tofocus the assessment on a specific stage prior to completion of aspecific action. As applied by autonomous system 3104, output asset 3120is received via process station N 3110 _(N). The autonomous system 3104can evaluate, as illustrated by 3146, a set of performance metrics{P^((C)) _(N-1→N)} leading to a specific degradation vector (not shown),based at least in part on an expected performance, for substantially alloperational components (e.g., tool, group or conglomerate tool) in theprocess station 3110 _(N). Additionally, it should be appreciated thatin process 3130, output assets (e.g., assets 3115) can be transportedacross disparate geographical areas, therefore the degradation vectorassessed by autonomous system 3104 can comprise metrics associated withthe in-transit portion of the process that leads to a partially finishedasset 3115. For example, when process 3130 regards semiconductorprocess, a workpiece may have less non-conformities or defects incertain process platforms. When result(s) 3149 of such an assessmentindicate that N-station output 3120 is faulty, autonomous system 3104isolates a faulty tool, or group of tools or platform, associated withprocess station N, and generates a report (e.g., assessment report 3150,repair(s) report 3160, or maintenance schedule 3170). The generatedreport(s) can contain information to be utilized by one or more actors(e.g., actors 1990 ₁-1990 _(Q)). In addition, reports can be stored tocreate a legacy of solutions (or “fixes”) or corrective processing forone or more manufacturing platforms for specific issues withperformance, especially issues that appear infrequently so that anactor's intervention can be preferred with respect to an autonomouslydeveloped solution which typically can benefit from extensivelyavailable data. Moreover, availability of reports can facilitate failuresimulations or forensic analysis of a failure episode, which can reducemanufacturing costs in at least two levels: (a) costly, infrequentlyfailing equipment can be predicted to fail under rare conditions, whichcan be simulated by autonomous system 1960, arising from operation ofequipment by an actor with a background non-commensurate with thecomplexity of the equipment, (b) optimization of parts inventory throughprediction of various failure scenarios based at least in part onhistorical data stored in assessment reports 3150 and repair reports3160.

In case results 3149 of process station N 3110 _(N) yield no faultytool, or group or platform of tools, assessment is performed on alower-level process station N-3110 _(N-1) that generates a partiallyprocessed output asset 3115 and is a part in the process cycle 3130 togenerate output 3120. Through analysis of a set of disparate performancemetrics {P^((C)) _(N-2→N-1)}, a degree of degradation can be extractedand associated tool, or group of tools (e.g., conglomerate C) can belocated. In instances that no faulty conglomerate of autonomous tools,or group of autonomous tools, or individual autonomous tool, autonomoussystem 3104 continues the backward, top-bottom assessment flow 3140 withthe object to locate sources of poor performance in final output 3120.

FIG. 32 is a block diagram of an example autonomous system 3200 whichcan distribute output assets that are autonomously generated by a toolconglomerate system. In system 3200, tool conglomerate 2920 _(Q) canautonomously generate a set of output assets 3210, which can be (i)information (e.g., structures and data patterns, relationships amongmeasured variables like a remedy to an existing degradation episode orcondition in alike or disparate tool groups that compose the autonomoustool conglomerate 2920 _(Q), and the like) gleaned or inferred about astate, including a performance degradation condition, of one or moretools that can compose tool conglomerate system 2920 _(Q); or (ii) anoutput product fabricated by said conglomerate. In addition, in system3200 output assets 3220 can be filtered by an asset selector 3220 andconveyed, or communicated, to a distribution component 3230. Suchdistribution component 3230 can exploit intelligent aspects ofautonomous biologically based learning system 1960. The distributioncomponent 3230 comprises a management component 3235 that can manipulatea packaging component 3245 and an encryption component 3255 that canprepare the data, as well as a scheduler 3265 and an asset monitor 3275.Packaging component 3245 can prepare the asset to be distributed for adistribution process; such preparation can include damage prevention aswell lost prevention. For information (e.g., an event in episodic memory3130 such as a system unwanted condition that develops as a result ofoperation outside a part specification like a temperature above athreshold) or data assets, packaging component 3245 can alter specificformats to present the information depending, at least partially, on theintended recipient of the asset to be distributed. For example,proprietary information can be abstract and presented withoutspecificity (e.g., explicit names of gases can be replaced with the word“gas;” relationships among specific parameters can be generalized to arelationship among variables such “p(O₂)<10⁻⁸ Torr” can be packaged as“p(gas)<10⁻⁸ Torr.”) In addition, packaging component 11645 can exploitan encryption component 3255 to ensure information integrity duringasset transmission and asset recovery at the intended recipient.

Additionally, in an aspect, management component 3235 can access (i) anasset store 3283, which typically contains assets scheduled to bedistributed or assets that have been distributed; (ii) a partner store3286 comprising commercial partners associated in the distribution orcompletion of specific assets; (iii) a customer store 3289 which cancontain current, past, or prospective customers to which the selectedasset has been, or can be distributed; (iv) a policy store that candetermine aspects associated to the distribution of assets, such aslicensing, customer support and relationships, procedures for assetpackaging, scheduling procedures, enforcement of intellectual propertyrights, and so on. It should be appreciated that information containedin policy store can change dynamically based at least in part onknowledge, e.g., information asset, learned or generated by autonomousbiologically based learning system.

Once an asset has been packaged and it has been scheduled fordistribution, a record of distribution can be stored, or if the asset isa data asset then a copy of the asset can be stored. Then, the asset canbe delivered to a disparate autonomous tool conglomerate P 2920 _(P).

FIG. 33 illustrates an example of autonomously determined distributionsteps, from design to manufacturing and to marketing, for an asset(e.g., a finished product, a partially finished product, . . . ). Thehexagonal cell 3310 represents a specific geographic area (e.g., a city,a county, a state, one or more countries) wherein two classes ofautonomous tool conglomerates; e.g., “circular” conglomerates 3320,3330, 3340, 3350, and 3360, and “square” conglomerates 3365 and 3375,participate in the manufacturing chain of a set of products, or assets.(It is to be noted that the geographical area can encompasssubstantially any bound area in addition to a hexagonal cell.) As anexample, scenario, and not by way of limitation, manufacturing of anasset starts at conglomerate 3320 which can be a conglomerate thatprovides design for custom-made solid state devices for opticalmanagement for high-mountain sports (e.g., skiing, climbing,paragliding, and so on). Design can consist in performing computationalsimulations of the optical properties of source materials and theircombinations, as well as device simulation. In such an instance,conglomerate 3320 can be a massively parallel supercomputer which can beconstrued in the subject example as a set of autonomous tool groups(FIG. 28), wherein each computer in the network of simulation computersis considered an autonomous tool group. Conglomerate 3320 outputs a oneor more designs of the optical device and a series of reports associatedwith description of the devices—e.g., a data asset. Such an output orasset (not shown), after appropriate encryption and packaging (e.g.,through component), can be conveyed to conglomerate 3330 via acommunication link 3324 which can be a wireless link.

Conglomerate 3330 can receive the data asset and, as a non-limitingexample, initiates a deposition process to fabricate a solid-statedevice according to the received asset. To that end, conglomerate 3330can partner with conglomerate 3340 and both can be regarded asfabrication facilities that are part of a two-conglomerate autonomousconglomerate tool 2910. Such conglomerates can produce multiple devicesaccording to the received specification asset, once a device isfabricated it can be tested, and assigned a quality and performancemetric, such metrics can lead to backward chaining to located “poorperformers” among the autonomous tools that enter conglomerates 3330 and3340. Through determination of multiple metrics, it is possible toautonomously adjust the operation of conglomerates 3320 and 3330 tooptimize production of the device, or output asset. It is noted thatlink 3324 indicates an internal link, wherein conglomerates 3330 and3340 are part of a same fabrication plant; thus, the asset can betransported in substantially different conditions than when utilizinglink 3324 which provides a vehicular transportation route. Link 3344 canbe employed to ship devices for commercial packaging in a disparategeographic location (such transportation can be motivated byadvantageous packaging costs, skillful labor, corporate tax incentives,and so on). It should be appreciated that an autonomous learning systemat conglomerate 3340 can optimize the shipping times (via a scheduler,for example) and routes (e.g., link 3344) in order to ensure timely andcost effective delivery. At conglomerate 3350 assets are packed andremotely tested, via a wireless link, in conglomerate 3360. In anaspect, the volume of devices tested and the lots from which devices aretested can be determined by an autonomous system in conglomerate 3360.Once packed devices have been approved for commercialization, the assetsare shipped through road link 3344 to conglomerate 3340, andsubsequently shipped via road link 3370 to a disparate class ofconglomerate 3375. Such conglomerate can be a partner vendor, andconglomerate 3375, a storage warehouse, which can be considered a toolgroup conglomerate. Such conglomerate is linked, internally, toconglomerate 3365 which can be a showroom for the received assets.

In view of the example systems presented and described above, amethodology that may be implemented in accordance with the disclosedsubject matter, will be better appreciated with reference to theflowchart of FIGS. 34, 35, and 36. While, for purposes of simplicity ofexplanation, the methodologies are shown and described as a series ofblocks, it is to be understood and appreciated that the disclosedaspects are not limited by the number or order of acts, as some acts mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedacts may be required to implement the methodologies describedhereinafter. It is to be appreciated that the functionality associatedwith the blocks may be implemented by software, hardware, a combinationthereof or any other suitable means (e.g., device, system, process,component). Additionally, it should be further appreciated that themethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such methodologies to various devices.Those skilled in the art will understand and appreciate that amethodology could alternatively be represented as a series ofinterrelated states or events, such as in a state diagram.

FIG. 34 presents a flowchart of an example method 3400 for biologicallybased autonomous learning with contextual goal adjustment. At act 3410 agoal is established. A goal is an abstraction associated with afunctionality of a goal component that is employed to accomplish thegoal or objective. A goal can be multi-disciplinary and span varioussectors (e.g., industrial, scientific, cultural, political, and so on).Generally, act 3410 can be executed by an actor that can be external, orextrinsic, to a goal component that can be coupled to a learning system(e.g., adaptive inference engine). In view of the multi-disciplinarynature of a goal, a goal component can be a tool, device, or system thatpossesses multiple functionalities; for instance, a tool system (e.g.,tool system 1910) that performs a specific process, or a device thatprovides with a specific outcome to a set of requests, or the like. Atact 3420 data is received such as measurement data of a workpiece. Suchdata can be intrinsic, e.g., data generated in a goal component (e.g.,component 1720) that pursues a goal. In an aspect, as a part ofperforming the specific process, a set of inspection systems withsensors or probes associated with the measurement module can gather thedata that is received in an adaptive intelligent component. Receiveddata can also be extrinsic, such as data conveyed by an actor (e.g.,actor 1990), which can be a human agent or a machine. Extrinsic data canbe data that is utilized to drive a process or, generally, to drive anaccomplishment of a specific goal. A human agent can be an operator ofthe tool system and can provide instructions or specific proceduresassociated with the processes performed by the tool. An example of anactor can be a computer performing a simulation of the tool system, orsubstantially any goal component. It should be appreciated thatsimulation of the tool system can be employed to determine deploymentparameters for the tool system, or for testing alternative conditions ofoperation for the tool (e.g., conditions of operations that can pose ahazard to a human agent or can be costly). The received data can betraining data or production data associated with a specific process, orgenerally a specific code.

In a further aspect, the received data can be associated with data typesor with procedural, or functional, units. A data type is a high levelabstraction of actual data; for instance, in an annealing state in thetool system a temperature can be controlled at a programmed level duringthe span of the annealing cycle, the time sequence of temperature valuesmeasured by a temperature sensor in the tool system can be associated asequence data type. Functional units can correspond to libraries ofreceived instructions, or processing code patches that manipulate datanecessary for the operation of the tool or for analyzing data generatedby the tool. Functional units can be abstracted into concepts related tothe specific functionality of the unit; for example, a multiplicationcode snippet can be abstracted into a multiply concept. Such conceptscan be overloaded, in that a single concept can be made dependent on aplurality of data types, such as multiply(sequence), multiply(matrix),or multiply(constant, matrix). Moreover, concepts associated withfunctional units can inherit other concepts associated with functionalunits, like derivative (scalar_product(vector, vector)) which canillustrate a concept that represents a derivative of a scalar product oftwo vectors with respect to an independent variable. It should beappreciated that functional concepts are in direct analogy with classes,which are in themselves concepts. Furthermore, data types can beassociated a priority and according to the priority can be deposited ina semantic network. Similarly, functional concepts (or autobots), canalso be associated with a priority, and deposited in a disparatesemantic network. Concept priorities are dynamic and can facilitateconcept activation in the semantic networks.

At act 3430 knowledge is generated from the received data, which can berepresented in semantic networks, as discussed above. Generation ofknowledge can be accomplished by propagating activation in the semanticnetworks. Such propagation can be determined by a situation scoreassigned to a concept in addition to a score combination. In an aspect,score combination can be a weighted addition of two scores, or anaverage of two or more scores. It should be appreciated that a rule forscore combination can be modified as necessary, depending on tool systemconditions or information input received from an external actor. Itshould be appreciated that a priority can decay as time progresses toallow concepts that are seldom activated to became obsolete, allowingnew concepts to become more relevant.

The generated knowledge can be complete information; for instance, asteady-state pressure in a deposition step is a precise, well-definedmathematical function (e.g., a single-valued function with allparameters that enter the function deterministically assessed, ratherthan being stochastic or unknown) of two independent variables likesteady-state flow and steady state exhaust valve angle. Alternatively,the generated knowledge can represent a partial understanding; forexample, an etch rate can be possess a known functional dependence ontemperature (e.g., an exponential dependence), yet the specificrelationship—e.g., precise values of parameters that determine thefunctional dependence—between etch rate and temperature is unknown.

At act 3440 the generated knowledge is stored for subsequent utilizationof for autonomous generation of further knowledge. In an aspect,knowledge can be stored in a hierarchy of memories. A hierarchy can bedetermined on the persistence of knowledge in the memory and thereadability of knowledge for creation of additional knowledge. In anaspect, a third tier in the hierarchy can be an episodic memory (e.g.,episodic memory 2130), wherein received data impressions and knowledgecan be collected. In such a memory tier manipulation of concepts is notsignificant, the memory acting instead as a reservoir of availableinformation received from a tool system or an external actor. In anaspect, such a memory can be identified as a meta database, in whichmultiple data types and procedural concepts can be stored. In a secondtier, knowledge can be stored in a short term memory wherein conceptscan be significantly manipulated and spread activation in semanticnetworks can take place. In such a memory tier, functional units orprocedural concepts operate on received data, and concepts to generatenew knowledge, or learning. A first tier memory can be a long termmemory (e.g., LTM 2110) in which knowledge is maintained for activeutilization, with significant new knowledge stored in this memory tier.In addition, knowledge in a long term memory can be utilized byfunctional units in short term memory.

At act 3450 the generated or stored knowledge is utilized. Knowledge canbe employed to (i) determine a level of degradation of a goal component(e.g., tool system 1910) by identifying differences between storedknowledge and newly received data (see self-awareness component 2150),wherein the received data can be extrinsic (e.g., input 1730) orintrinsic (e.g., a portion of output 1740); (ii) characterize eitherextrinsic or intrinsic data or both, for example by identifying datapatterns or by discovering relationships among variables (such as in aself-conceptualization component 2160), wherein the variables can beutilized to accomplish the established goal; or (iii) generate ananalysis of the performance of the tool system that generates the data(e.g., self-optimization component 2170), providing indications of rootcause for predicted failures or existing failures as well as necessaryrepairs or triggering alarms for implementing preventive maintenancebefore degradation of the tool system causes tool failure. It is to benoted that utilization of the stored and generated knowledge is affectedby the received data—extrinsic or intrinsic—and the ensuing generatedknowledge.

Act 3460 is a validation act in which the degree of accomplishment of agoal can be inspected in view of generated knowledge. In case theestablished goal is accomplished, example method 3400 can end.Alternatively, if the established goal has not been accomplished, theestablished goal can be reviewed at act 3470. In the latter, flow ofmethod 2400 can lead to establishing a new goal in case a current goalis to be revised or adapted; for instance, goal adaptation can be basedon generated knowledge. In case no revision of a current goal is to bepursued, flow of method 3400 is returned to generate knowledge, whichcan be utilized to continue pursuing the currently established goal.

FIG. 35 presents a flowchart 3500 of an example method for adjusting asituation score of a concept associated with a state of a goalcomponent. At act 3510 a state of a goal component is determined a statetypically is established through a context, which can be determined byvarious data input (e.g., input 1730), or through a network of conceptsassociated with the input and exhibiting specific relationships. Theinput data relates to a goal that is pursued by the goal component; forinstance, a recipe for a coating process of a specific thin-film devicecan be deemed as input associated with a “deposit an insulating device”goal. At act 3520 a set of concepts that can be applied to the state ofthe goal component is determined. Such concepts can be abstractions ofdata types entered in act 3510 or can be existing concepts in a memoryplatform (e.g., long term memory 2110, or short term memory 2120).Generally, functional concepts that can act on descriptive concepts(e.g., concepts with no functional component) can be utilized morefrequently towards achieving a goal. At act 3530 a situation score foreach concept in a set of concepts associated with the goal state isdetermined a set of situation scores can establish a hierarchy forconcept utilization or application, which can determine the dynamics ofa goal, like goal adaptation or sub-goal creation/randomization.Adjustment of situation scores for specific concepts can drive goalaccomplishment as well as propagation within a space of goals as part ofgoal adaptation.

FIG. 36 presents a flowchart 3600 of an example method for generatingknowledge through inference. At act 3610 a concept is associated to adata type and a priority for the concept is determined. Prioritiestypically can be determined based on a probability of utilization of aconcept, or a concept's weight. Such a weight can be determined througha function (e.g., a weighted sum, or a geometric average) of parametersthat can represent the ease to utilize a concept (e.g., the complexityto operate on a data type), such a parameter can be identified with aconcept's inertia, and the suitability parameter of a concept todescribe a state (e.g., a number of neighboring concepts that can berelated the concept). It should be appreciated that a priority can betime dependent as a consequence of explicitly time-dependent inertia andsuitability parameters, or as a result of concept propagation. Timedependent priorities can introduce aging aspects into specific conceptsand thus can promote knowledge flexibility (e.g., knowledge (forexample, a paradigm employed to pursue a goal, such as a recipe forpreparation of a nano-structured device) through concepts ceasing to berelevant in a particular knowledge scenario (e.g., node structure in apriority-based knowledge network). At act 3620 a semantic network for aset of prioritized concepts is established. It should be appreciatedthat the semantic network can comprise multiple sub-networks, whereineach of the multiple networks can characterize a set of relationshipsamong concepts in a class. As an example, in a two-tier semanticnetwork, a first sub-network can represent relationships among conceptsderived from data types, whereas a second sub-network can compriserelationships among functional concepts (e.g., a planner autobot orüberbot, a conceptual autobot) describing operations that can beutilized to alter upon a data type. At act 3630 the set of priorities ispropagated over the semantic network to make an inference and thusgenerate knowledge associated with the network of concepts. In anaspect, such propagation can be utilized to generate optimization plansfor goal adaptation, or to predict failures in a system that pursues aspecific goal.

FIG. 37 is a flowchart of an example method 3700 for asset distribution.Asset(s) can be provided by an individual autonomous tool, an autonomousgroup tool (e.g., system 2810), or an autonomous conglomerated toolsystem (e.g., system 2910). It should be appreciated that assets can begenerated in alternative manners as well. At act 3710 an asset isreceived. In an aspect, the received asset can be an asset selected fromoutput asset(s) generated by one or more autonomous tools. At act 3720the received asset is processed for distribution. As discussed above, anasset typically carries advantages associated with knowledge utilized ingenerating the asset; thus, an asset can be packaged in such a mannerthat prevent a competitor to reverse-engineer the asset. It should beappreciated that depending on the destination of the asset, packaginginformation associated to the asset can be customized, deliveringdisparate levels of information based at least in part on whether theentity that receives the asset is a commercial partner, or a customer,or other branch, division, or group of an organization that fabricatesthe asset. The level of information packaged with the asset can followspecific policies (for example, policies stored in policy store 3292).Additionally, for data assets or computer program assets, such assetscan be encrypted while being packaged in order retain integrity of theinformation conveyed by the asset. Moreover, part of the processing fordistributing an asset can include retaining the asset in storage (e.g.,asset store 3283) while a suitable distribution schedule is followed. Inan aspect, such schedule can be optimized by an autonomous system (e.g.,system 2960) that supports a tools system the fabricates, or produces,the asset to be distributed.

At act 3730 the processed asset is distributed. Distribution typicallydepends on the asset features and characteristics, as well as on thedestination of the asset. For example, assets can be distributed withina factory plant, in order to complete asset production like in anassembly line wherein an unfinished vehicle (e.g., an asset) can betransported through different stages of assembly. Similarly, in the foodindustry, a frozen meal (e.g., asset) is distributed throughout a foodpreparation plant. Alternatively, or in addition, depending on industry,an unfinished asset can be distributed to overseas to be finished inorder to benefit from cost-effective production markets.

At act 3740, a distributed asset is monitored in order to ensure, forexample, the asset distribution adheres to applicable distributionregulation, or to ensure adequate inventory replenishment by havingaccess to distribution status of the asset. In addition, monitoringdistribution of the asset can mitigate losses and damages, as well ascan facilitate interaction with commercial partners and customers.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What has been described above includes examples of the claimed subjectmatter. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe claimed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the claimedsubject matter are possible. Accordingly, the claimed subject matter isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

The invention claimed is:
 1. A manufacturing platform for processing aworkpiece for the fabrication of electronic devices thereon, using aprocessing system, the manufacturing platform comprising: a plurality ofprocessing modules hosted on a common manufacturing platform, theprocessing modules configured for manipulating materials on a workpiecein processing steps as part of a processing sequence; the plurality ofprocessing modules including a first module and a second module, thefirst and second modules facilitating different processes in the processsequence; at least one measurement module hosted on the commonmanufacturing platform, the measurement module including an inspectionsystem operable for measuring data associated with an attribute of theworkpiece at least one of before or after the workpiece is processed ina processing module of the common manufacturing platform; at least oneworkpiece transfer module hosted on the common manufacturing platformand configured for movement of the workpiece between the processingmodules and the at least one measurement module; an active interdictioncontrol system hosted at least in part on the common manufacturingplatform and coupled with the measurement module, the activeinterdiction control system configured for processing the measured dataassociated with an attribute on the workpiece for detectingnon-conformities and configured to perform corrective processing of theworkpiece at least in part in a processing module upstream and/ordownstream in the process sequence when non-conformities are detected;the active interdiction control system further configured forcontrolling movement and processing of the workpiece in the processingsequence; the common manufacturing platform, the processing modules, andthe measurement module operating in a controlled environment, and theworkpiece transfer module configured for transferring the workpiecebetween the plurality of processing modules in the processing sequenceand the measurement module without leaving the controlled environment.2. The manufacturing platform of claim 1, wherein the first module is afilm-forming module, and the second module is an etch module.
 3. Themanufacturing platform of claim 1, wherein the active interdictioncontrol system includes a pattern recognition component to extract andclassify data patterns from the measured data, and predict an existenceof a non-conformity.
 4. The manufacturing platform of claim 3, whereinthe pattern recognition component comprises a deep learningarchitecture.
 5. The manufacturing platform of claim 4, wherein thepattern recognition component correlates an extracted data pattern witha learned attribute on the workpiece.
 6. The manufacturing platform ofclaim 5, wherein the learned attribute includes a defect on theworkpiece.
 7. The manufacturing platform of claim 6, wherein the defectincludes an out-of-tolerance condition for an attribute, the attributeincluding a thickness, a critical dimension, a surface roughness, a filmor surface composition, a feature profile, a pattern edge placement, avoid, a loss of selectivity, a measure of non-uniformity, or a loadingeffect, or any combination of two or more thereof.
 8. The manufacturingplatform of claim 1, wherein the active interdiction control systemfurther includes a display component to show a region of the workpiecewhere the non-conformity exists.
 9. The manufacturing platform of claim1, wherein the active interdiction control system includes a correlationcomponent to predict an existence of a non-conformity based upon acorrelation of measured data at two or more locations on the workpiece.10. The manufacturing platform of claim 1, wherein the activeinterdiction control system includes an autonomous learning componentthat receives the measured data and generates knowledge based, at leastin part, on (i) characterizes the measured data and performance of theprocess sequence, and (ii) decides an action plan to correct the processsequence in the event the non-conformity exists.
 11. The manufacturingplatform of claim 10, wherein the autonomous learning component executesat least one of supervised learning, clustering, dimensionalityreduction, structured prediction, anomaly detection, or reinforcementlearning, or any combination of two or more thereof.
 12. Themanufacturing platform of claim 1, wherein the active interdictioncontrol system comprises: an interaction component that receives themeasured data, the interaction component including an adaptor componentthat packages the measured data and conveys packaged data; an autonomouslearning component that receives the packaged data and generates aknowledge that characterizes the packaged data and performance of theprocess sequence.
 13. The manufacturing platform of claim 12, whereinthe autonomous learning component includes: a processing platform thatprocesses the packaged data, the processing platform includes a set offunctional units that operate on the packaged data, wherein the set offunctional units comprise: an adaptive inference engine that analyzesthe packaged data and infers an action to perform based at least in parton a process goal for the process sequence; and a goal component thatevolves the process goal based at least in part on one of data or acontext change; and a memory platform that stores the knowledge, thememory platform includes a hierarchy of memories that includes a longterm memory, a short term memory, and an episodic memory, wherein thelong term memory stores a set of concepts that includes at least one ofan entity, a relationship, or a procedure, and wherein a concept in theset of concepts includes a first numeric attribute that indicatesrelevance of a concept to a current state of the process sequence, and asecond numeric attribute that indicates a degree of difficulty to usethe concept.
 14. The manufacturing platform of claim 13, wherein theinteractive component further receives module diagnostic data from oneor more of the plurality of the processing modules, and packages themodule diagnostic data with the measured data when preparing thepackaged data.
 15. The manufacturing platform of claim 13 wherein theinteraction component further comprises an interaction manager thatfacilitates data exchange with an external actor.
 16. The manufacturingplatform of claim 15, wherein at least one of packaged data or the dataexchanged with the external actor includes training data.
 17. Themanufacturing platform of claim 16, wherein the training data furtherincludes at least one of an identification of a module process orvariable associated with a task, a functional relationship among two ormore module processes or variables associated with a task, a causalgraph that includes a set of a priori probabilities associated with aset of module processes or variables related to the task and present inthe causal graph and a set of conditional probabilities that relate oneor more module processes or variables related to the task and present inthe causal graph, or a set of parameters that describe a behavior of theprocess sequence.